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Konstantinos Nikolopoulos

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Konstantinos Nikolopoulos & Konstantia Litsiou, 2019. "Consumer payment choice during the crisis in Europe: a heterogeneous behaviour?," Working Papers 19007, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).

    Cited by:

    1. Konstantinos Nikolopoulos & Konstantia Litsiou, 2019. "When the bank is closed, the cash is king; ... not!," Working Papers 19008, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).

  2. Dinesh Reddy Vangumalli & Konstantinos Nikolopoulos & Konstantia Litsiou, 2019. "Clustering, Forecasting and Cluster Forecasting: using k-medoids, k-NNs and random forests for cluster selection," Working Papers 19016, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).

    Cited by:

    1. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.

  3. Konstantinos Nikolopoulos & Fotios Petropoulos & Vasco Sanchez Rodrigues & Stephen Pettit & Anthony Beresford, 2019. "A risk-mitigation model driven from the level of forecastability of Black Swans: prepare and respond to major Earthquakes through a dynamic Temporal and Spatial Aggregation forecasting framework," Working Papers 19017, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).

    Cited by:

    1. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.

  4. Kostas Nikolopoulos & F. Petropoulos, 2015. "Forecasting, Foresight and Strategic Planning for Black Swans," Working Papers 15003, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).

    Cited by:

    1. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.

  5. Dimitrios D. Thomakos & Konstantinos Nikolopoulos, 2013. "Forecasting multivariate time series with the Theta Method," Working Papers 13004, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).

    Cited by:

    1. Sbrana, Giacomo & Silvestrini, Andrea, 2023. "The RWDAR model: A novel state-space approach to forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 922-937.
    2. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2019. "Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors," International Journal of Production Economics, Elsevier, vol. 209(C), pages 92-102.
    3. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
    5. Fiorucci, Jose A. & Pellegrini, Tiago R. & Louzada, Francisco & Petropoulos, Fotios & Koehler, Anne B., 2016. "Models for optimising the theta method and their relationship to state space models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1151-1161.
    6. Kyriazi, Foteini & Thomakos, Dimitrios D. & Guerard, John B., 2019. "Adaptive learning forecasting, with applications in forecasting agricultural prices," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1356-1369.

  6. Konstantinos Nikolopoulos & Dimitrios Thomakos & Fotios Petropoulos & Vassilis Assimakopoulos, 2009. "Theta Model Forecasts for Financial Time Series: A Case Study in the S&P500," Working Papers 0033, University of Peloponnese, Department of Economics.

    Cited by:

    1. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.

  7. Elli Pagourtzi & Kostantinos Nikolopoulos, 2004. "Architecture for a real estate analysis information system using GIS techniques integrated with Fuzzy theory," ERES eres2004_193, European Real Estate Society (ERES).

    Cited by:

    1. d’Amato, Maurizio & Zrobek, Sabina & Renigier Bilozor, Malgorzata & Walacik, Marek & Mercadante, Giuseppe, 2019. "Valuing the effect of the change of zoning on underdeveloped land using fuzzy real option approach," Land Use Policy, Elsevier, vol. 86(C), pages 365-374.

Articles

  1. Katsagounos, Ilias & Thomakos, Dimitrios D. & Litsiou, Konstantia & Nikolopoulos, Konstantinos, 2021. "Superforecasting reality check: Evidence from a small pool of experts and expedited identification," European Journal of Operational Research, Elsevier, vol. 289(1), pages 107-117.

    Cited by:

    1. Bonazzi, Riccardo & Viscusi, Gianluigi & Solidoro, Adriano, 2024. "Crowd mining as a strategic resource for innovation seekers," Technovation, Elsevier, vol. 132(C).
    2. Angelopoulos, Spyros & Bendoly, Elliot & Fransoo, Jan C. & Hoberg, Kai & Ou, Carol & Tenhiälä, Antti, 2023. "Digital transformation in operations management: Fundamental change through agency reversal," Other publications TiSEM 373742f5-0b87-4276-9ed6-8, Tilburg University, School of Economics and Management.
    3. Karvetski, Christopher W. & Meinel, Carolyn & Maxwell, Daniel T. & Lu, Yunzi & Mellers, Barbara A. & Tetlock, Philip E., 2022. "What do forecasting rationales reveal about thinking patterns of top geopolitical forecasters?," International Journal of Forecasting, Elsevier, vol. 38(2), pages 688-704.

  2. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.

    Cited by:

    1. Ducharme, Corey & Agard, Bruno & Trépanier, Martin, 2021. "Forecasting a customer's Next Time Under Safety Stock," International Journal of Production Economics, Elsevier, vol. 234(C).
    2. Andrea Kolková & Aleksandr Kljuènikov, 2021. "Demand forecasting: an alternative approach based on technical indicator Pbands," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 1063-1094, December.
    3. Prak, Dennis & Rogetzer, Patricia, 2022. "Timing intermittent demand with time-varying order-up-to levels," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1126-1136.
    4. Li Li & Yanfei Kang & Fotios Petropoulos & Feng Li, 2022. "Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications," Papers 2204.08283, arXiv.org, revised Aug 2022.
    5. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
    6. Schlaich, Tim & Hoberg, Kai, 2024. "When is the next order? Nowcasting channel inventories with Point-of-Sales data to predict the timing of retail orders," European Journal of Operational Research, Elsevier, vol. 315(1), pages 35-49.
    7. G. Peter Zhang & Yusen Xia & Maohua Xie, 2024. "Intermittent demand forecasting with transformer neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 1051-1072, August.
    8. Jože Martin Rožanec & Blaž Fortuna & Dunja Mladenić, 2022. "Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    9. Corey Ducharme & Bruno Agard & Martin Trépanier, 2024. "Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1661-1681, August.
    10. Pérez, Eduardo & Marthak, Yash V. & Méndez Mediavilla, Francis A., 2023. "Analysis and forecast of donations at domestic hunger relief organizations impacted by natural disasters," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    11. Sarlo, Rodrigo & Fernandes, Cristiano & Borenstein, Denis, 2023. "Lumpy and intermittent retail demand forecasts with score-driven models," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1146-1160.
    12. Das, Saikat & Bose, Indranil & Sarkar, Uttam Kumar, 2023. "Predicting the outbreak of epidemics using a network-based approach," European Journal of Operational Research, Elsevier, vol. 309(2), pages 819-831.

  3. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.

    Cited by:

    1. Xie, Lei & Hou, Pengwen & Han, Hongshuai, 2021. "Implications of government subsidy on the vaccine product R&D when the buyer is risk averse," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    2. Ramani, Vinay & Ghosh, Debabrata & Sodhi, ManMohan S., 2022. "Understanding systemic disruption from the Covid-19-induced semiconductor shortage for the auto industry," Omega, Elsevier, vol. 113(C).
    3. María Arquer & Borja Ponte & Raúl Pino, 2022. "Examining the balance between efficiency and resilience in closed-loop supply chains," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(4), pages 1307-1336, December.
    4. Ranveer Singh Rana & Dinesh Kumar & Kanika Prasad & K. Mathiyazhagan, 2024. "Mitigating the impact of demand disruption on perishable inventory in a two-warehouse system," Operations Management Research, Springer, vol. 17(2), pages 469-504, June.
    5. Hammami, Ramzi & Salman, Sinan & Khouja, Moutaz & Nouira, Imen & Alaswad, Suzan, 2023. "Government strategies to secure the supply of medical products in pandemic times," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1364-1387.
    6. Víctor Giménez & Diego Prior & Claudio Thieme & Emili Tortosa-Ausina, 2021. "International comparisons of the COVID-19 pandemic management: What can be learned from activity analysis techniques?," Working Papers 2021/12, Economics Department, Universitat Jaume I, Castellón (Spain).
    7. Behzad Vahedi & Morteza Karimzadeh & Hamidreza Zoraghein, 2021. "Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    8. Bernardo Melo Pimentel & Guillermo Hunter Ramírez, 2022. "Davids and Goliaths: Hidden champions in an age of state capitalism," Working Papers hal-03685959, HAL.
    9. Rishi Patel, 2023. "The Transformation of the Healthcare Business through the COVID-19 Pandemic (2020–2021)," JRFM, MDPI, vol. 16(7), pages 1-13, July.
    10. Nourhan Ah. Saad & Sara Elgazzar & Sonja Mlaker Kac, 2022. "Investigating the Impact of Resilience, Responsiveness, and Quality on Customer Loyalty of MSMEs: Empirical Evidence," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    11. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    12. Li, Dong & Dong, Chuanwen, 2022. "Government regulations to mitigate the shortage of life-saving goods in the face of a pandemic," European Journal of Operational Research, Elsevier, vol. 301(3), pages 942-955.
    13. Brusset, Xavier & Ivanov, Dmitry & Jebali, Aida & La Torre, Davide & Repetto, Marco, 2023. "A dynamic approach to supply chain reconfiguration and ripple effect analysis in an epidemic," International Journal of Production Economics, Elsevier, vol. 263(C).
    14. Bekker, René & uit het Broek, Michiel & Koole, Ger, 2023. "Modeling COVID-19 hospital admissions and occupancy in the Netherlands," European Journal of Operational Research, Elsevier, vol. 304(1), pages 207-218.
    15. Hu, Man & Liu, Xue-Xin & Jia, Fu, 2024. "Optimal Emergency Order Policy for Supply Disruptions in the Semiconductor Industry," International Journal of Production Economics, Elsevier, vol. 272(C).
    16. Delis, Manthos D. & Iosifidi, Maria & Tasiou, Menelaos, 2021. "Efficiency of government policy during the COVID-19 pandemic," MPRA Paper 107046, University Library of Munich, Germany.
    17. Ardekani, Zahra Fozouni & Sobhani, Seyed Mohammad Javad & Barbosa, Marcelo Werneck & de Sousa, Paulo Renato, 2023. "Transition to a sustainable food supply chain during disruptions: A study on the Brazilian food companies in the Covid-19 era," International Journal of Production Economics, Elsevier, vol. 257(C).
    18. Fariba Goodarzian & Peiman Ghasemi & Angappa Gunasekaren & Ata Allah Taleizadeh & Ajith Abraham, 2022. "A sustainable-resilience healthcare network for handling COVID-19 pandemic," Annals of Operations Research, Springer, vol. 312(2), pages 761-825, May.
    19. Szalkowski, Gabriel Andy & Mikalef, Patrick, 2023. "Understanding digital platform evolution using compartmental models," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    20. Taiwen Feng & Zhihui Si & Wenbo Jiang & Jianyu Tan, 2024. "Supply chain transformational leadership and resilience: the mediating role of ambidextrous business model," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    21. Zhisong Chen & Xiaoying Niu & Qingwu Gao & Jun Wang, 2024. "Bilateral shock effect and alleviation strategy: a game-theoretical study in international medical devices supply chain in the pandemic context," Operational Research, Springer, vol. 24(2), pages 1-37, June.
    22. Delis, Manthos D. & Savva, Christos S. & Theodossiou, Panayiotis, 2021. "The impact of the coronavirus crisis on the market price of risk," Journal of Financial Stability, Elsevier, vol. 53(C).
    23. Lilia A. Valitova & Elena R. Sharko & Marina Yu. Sheresheva, 2021. "Identifying industrial clusters based on the analysis of business ties: A case of the textile industry," Upravlenets, Ural State University of Economics, vol. 12(4), pages 59-74, September.
    24. Mohammad Ebrahim Arbabian & Hossein Rikhtehgar Berenji, 2023. "Inventory systems with uncertain supplier capacity: an application to covid-19 testing," Operations Management Research, Springer, vol. 16(1), pages 324-344, March.
    25. Kazim Topuz & Behrooz Davazdahemami & Dursun Delen, 2024. "A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases," Annals of Operations Research, Springer, vol. 341(1), pages 673-697, October.
    26. Vahdani, Behnam & Mohammadi, Mehrdad & Thevenin, Simon & Gendreau, Michel & Dolgui, Alexandre & Meyer, Patrick, 2023. "Fair-split distribution of multi-dose vaccines with prioritized age groups and dynamic demand: The case study of COVID-19," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1249-1272.
    27. Alessandro Bitetto & Paola Cerchiello & Charilaos Mertzanis, 2021. "A data-driven approach to measuring epidemiological susceptibility risk around the world," DEM Working Papers Series 200, University of Pavia, Department of Economics and Management.
    28. Benítez-Peña, Sandra & Carrizosa, Emilio & Guerrero, Vanesa & Jiménez-Gamero, M. Dolores & Martín-Barragán, Belén & Molero-Río, Cristina & Ramírez-Cobo, Pepa & Romero Morales, Dolores & Sillero-Denami, 2021. "On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19," European Journal of Operational Research, Elsevier, vol. 295(2), pages 648-663.
    29. Fildes, Robert & Kolassa, Stephan & Ma, Shaohui, 2022. "Post-script—Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1319-1324.
    30. Tomas Baležentis & Mangirdas Morkūnas & Agnė Žičkienė & Artiom Volkov & Erika Ribašauskienė & Dalia Štreimikienė, 2021. "Policies for Rapid Mitigation of the Crisis’ Effects on Agricultural Supply Chains: A Multi-Criteria Decision Support System with Monte Carlo Simulation," Sustainability, MDPI, vol. 13(21), pages 1-31, October.
    31. Mi Yan & Qingmiao Li & Jiazhen Zhang, 2023. "Rethinking Industrial Heritage Tourism Resources in the EU: A Spatial Perspective," Land, MDPI, vol. 12(8), pages 1-32, July.
    32. Sabah Bushaj & Xuecheng Yin & Arjeta Beqiri & Donald Andrews & İ. Esra Büyüktahtakın, 2023. "A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization," Annals of Operations Research, Springer, vol. 328(1), pages 245-277, September.
    33. Taylor, James W. & Taylor, Kathryn S., 2023. "Combining probabilistic forecasts of COVID-19 mortality in the United States," European Journal of Operational Research, Elsevier, vol. 304(1), pages 25-41.
    34. Liu, Jia & Bai, Jinyu & Wu, Desheng, 2021. "Medical supplies scheduling in major public health emergencies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    35. Mehdi Alizadeh & Mir Saman Pishvaee & Hamed Jahani & Mohammad Mahdi Paydar & Ahmad Makui, 2023. "Viable healthcare supply chain network design for a pandemic," Annals of Operations Research, Springer, vol. 328(1), pages 35-73, September.
    36. Elif Bozkaya & Levent Eriskin & Mumtaz Karatas, 2023. "Data analytics during pandemics: a transportation and location planning perspective," Annals of Operations Research, Springer, vol. 328(1), pages 193-244, September.
    37. Vu Minh Ngo & Huan Huu Nguyen & Hiep Cong Pham & Hung Manh Nguyen & Phuc Vinh Dang Truong, 2023. "Digital supply chain transformation: effect of firm’s knowledge creation capabilities under COVID-19 supply chain disruption risk," Operations Management Research, Springer, vol. 16(2), pages 1003-1018, June.
    38. Arben Asllani & Silvana Trimi, 2022. "COVID-19 vaccine distribution: exploring strategic alternatives for the greater good," Service Business, Springer;Pan-Pacific Business Association, vol. 16(3), pages 601-619, September.
    39. Yicheol Han & Stephan J. Goetz & Claudia Schmidt, 2021. "Visualizing Spatial Economic Supply Chains to Enhance Sustainability and Resilience," Sustainability, MDPI, vol. 13(3), pages 1-15, February.
    40. Choudhury, Nishat Alam & Ramkumar, M. & Schoenherr, Tobias & Singh, Shalabh, 2023. "The role of operations and supply chain management during epidemics and pandemics: Potential and future research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    41. Wu, Binrong & Wang, Lin & Wang, Sirui & Zeng, Yu-Rong, 2021. "Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic," Energy, Elsevier, vol. 226(C).
    42. Qi, Yue & Liao, Kezhi & Liu, Tongyang & Zhang, Yu, 2022. "Originating multiple-objective portfolio selection by counter-COVID measures and analytically instigating robust optimization by mean-parameterized nondominated paths," Operations Research Perspectives, Elsevier, vol. 9(C).
    43. Camacho, Carmen & Vasilakis, Chrysovalantis, 2023. "Transmissible Diseases, Vaccination and Inequality," IZA Discussion Papers 16504, Institute of Labor Economics (IZA).
    44. Xi, Mengjie & Liu, Yang & Fang, Wei & Feng, Taiwen, 2024. "Intelligent manufacturing for strengthening operational resilience during the COVID-19 pandemic: A dynamic capability theory perspective," International Journal of Production Economics, Elsevier, vol. 267(C).
    45. Timo Kuosmanen & Yong Tan & Sheng Dai, 2023. "Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic," Health Care Management Science, Springer, vol. 26(3), pages 447-460, September.
    46. Aljuneidi, Tariq & Punia, Sushil & Jebali, Aida & Nikolopoulos, Konstantinos, 2024. "Forecasting and planning for a critical infrastructure sector during a pandemic: Empirical evidence from a food supply chain," European Journal of Operational Research, Elsevier, vol. 317(3), pages 936-952.
    47. Ksciuk, Jana & Kuhlemann, Stefan & Tierney, Kevin & Koberstein, Achim, 2023. "Uncertainty in maritime ship routing and scheduling: A Literature review," European Journal of Operational Research, Elsevier, vol. 308(2), pages 499-524.
    48. Dubey, Rameshwar & Bryde, David J. & Dwivedi, Yogesh K. & Graham, Gary & Foropon, Cyril & Papadopoulos, Thanos, 2023. "Dynamic digital capabilities and supply chain resilience: The role of government effectiveness," International Journal of Production Economics, Elsevier, vol. 258(C).
    49. Vahdani, Behnam & Mohammadi, Mehrdad & Thevenin, Simon & Meyer, Patrick & Dolgui, Alexandre, 2023. "Production-sharing of critical resources with dynamic demand under pandemic situation: The COVID-19 pandemic," Omega, Elsevier, vol. 120(C).
    50. Huberts, Nick F.D. & Thijssen, Jacco J.J., 2023. "Optimal timing of non-pharmaceutical interventions during an epidemic," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1366-1389.
    51. Ali, Usman & Li, Yanxi & Wang, Jian-Jun & Yue, Xiaohang & Chang, Ai-Chih (Jasmine), 2021. "Dynamics of outward FDI and productivity spillovers in logistics services industry: Evidence from China," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    52. Alexey I. Borovkov & Marina V. Bolsunovskaya & Aleksei M. Gintciak, 2022. "Intelligent Data Analysis for Infection Spread Prediction," Sustainability, MDPI, vol. 14(4), pages 1-11, February.
    53. Das, Saikat & Bose, Indranil & Sarkar, Uttam Kumar, 2023. "Predicting the outbreak of epidemics using a network-based approach," European Journal of Operational Research, Elsevier, vol. 309(2), pages 819-831.
    54. V C Parro & M L M Lafetá & F Pait & F B Ipólito & T N Toporcov, 2021. "Predicting COVID-19 in very large countries: The case of Brazil," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-15, July.
    55. Kedwadee Sombultawee & Pattama Lenuwat & Natdanai Aleenajitpong & Sakun Boon-itt, 2022. "COVID-19 and Supply Chain Management: A Review with Bibliometric," Sustainability, MDPI, vol. 14(6), pages 1-21, March.
    56. Duong An & Duy Tran Le Anh & Huong Le Thi Cam & Rajkishore Nayak & Majo George & Loan Bui Thi Cam & Nhu-Y Ngoc Hoang & Duy Tan Nguyen & Huy Truong Quang, 2024. "Navigating global supply networks: a strategic framework for resilience in the apparel industry," Operations Management Research, Springer, vol. 17(2), pages 523-543, June.
    57. Hosseini-Motlagh, Seyyed-Mahdi & Samani, Mohammad Reza Ghatreh & Homaei, Shamim, 2023. "Design of control strategies to help prevent the spread of COVID-19 pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 219-238.
    58. Balezentis, Tomas & Zickiene, Agne & Volkov, Artiom & Streimikiene, Dalia & Morkunas, Mangirdas & Dabkiene, Vida & Ribasauskiene, Erika, 2023. "Measures for the viable agri-food supply chains: A multi-criteria approach," Journal of Business Research, Elsevier, vol. 155(PA).
    59. Margherita Molinaro & Pietro Romano & Gianluca Sperone, 2023. "The organizational side of a disruption mitigation process: exploring a case study during the COVID-19 pandemic," Operations Management Research, Springer, vol. 16(1), pages 1-17, March.

  4. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.

    Cited by:

    1. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    2. Paweł Piotrowski & Marcin Kopyt & Dariusz Baczyński & Sylwester Robak & Tomasz Gulczyński, 2021. "Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine," Energies, MDPI, vol. 14(5), pages 1-25, February.

  5. Karamaziotis, Panagiotis I. & Raptis, Achilleas & Nikolopoulos, Konstantinos & Litsiou, Konstantia & Assimakopoulos, Vassilis, 2020. "An empirical investigation of water consumption forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(2), pages 588-606.

    Cited by:

    1. Azar Niknam & Hasan Khademi Zare & Hassan Hosseininasab & Ali Mostafaeipour & Manuel Herrera, 2022. "A Critical Review of Short-Term Water Demand Forecasting Tools—What Method Should I Use?," Sustainability, MDPI, vol. 14(9), pages 1-25, April.

  6. Sushil Punia & Konstantinos Nikolopoulos & Surya Prakash Singh & Jitendra K. Madaan & Konstantia Litsiou, 2020. "Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail," International Journal of Production Research, Taylor & Francis Journals, vol. 58(16), pages 4964-4979, July.

    Cited by:

    1. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.
    2. Thais de Castro Moraes & Jiancheng Qin & Xue-Ming Yuan & Ek Peng Chew, 2023. "Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions," Logistics, MDPI, vol. 7(4), pages 1-18, November.
    3. Truong Ngoc Cuong & Le Ngoc Bao Long & Hwan-Seong Kim & Sam-Sang You, 2023. "Data analytics and throughput forecasting in port management systems against disruptions: a case study of Busan Port," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 61-89, March.
    4. Carlos Cuartas & Jose Aguilar, 2023. "Hybrid algorithm based on reinforcement learning for smart inventory management," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 123-149, January.
    5. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    6. Naragain Phumchusri & Nichakan Phupaichitkun, 2024. "Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(5), pages 461-480, October.
    7. Thais de Castro Moraes & Xue‐Ming Yuan & Ek Peng Chew, 2024. "Hybrid convolutional long short‐term memory models for sales forecasting in retail," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1278-1293, August.
    8. Paweł Więcek & Daniel Kubek, 2024. "The Impact Time Series Selected Characteristics on the Fuel Demand Forecasting Effectiveness Based on Autoregressive Models and Markov Chains," Energies, MDPI, vol. 17(16), pages 1-18, August.
    9. Joanna Henzel & Łukasz Wróbel & Marcin Fice & Marek Sikora, 2022. "Energy Consumption Forecasting for the Digital-Twin Model of the Building," Energies, MDPI, vol. 15(12), pages 1-21, June.
    10. Azadi, Majid & Yousefi, Saeed & Farzipoor Saen, Reza & Shabanpour, Hadi & Jabeen, Fauzia, 2023. "Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis," Journal of Business Research, Elsevier, vol. 154(C).
    11. Omar, Haytham & Klibi, Walid & Babai, M. Zied & Ducq, Yves, 2023. "Basket data-driven approach for omnichannel demand forecasting," International Journal of Production Economics, Elsevier, vol. 257(C).
    12. Keun Hee Lee & Mali Abdollahian & Sergei Schreider & Sona Taheri, 2023. "Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect," Mathematics, MDPI, vol. 11(11), pages 1-28, May.
    13. Aljuneidi, Tariq & Punia, Sushil & Jebali, Aida & Nikolopoulos, Konstantinos, 2024. "Forecasting and planning for a critical infrastructure sector during a pandemic: Empirical evidence from a food supply chain," European Journal of Operational Research, Elsevier, vol. 317(3), pages 936-952.
    14. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    15. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.
    16. Li, Xishu & Yin, Ying & Manrique, David Vergara & Bäck, Thomas, 2021. "Lifecycle forecast for consumer technology products with limited sales data," International Journal of Production Economics, Elsevier, vol. 239(C).
    17. Mohammadhanif Dasoomi & Ali Naderan & Tofigh Allahviranloo, 2023. "Predicting the Choice of Online or Offline Shopping Trips Using a Deep Neural Network Model and Time Series Data: A Case Study of Tehran, Iran," Sustainability, MDPI, vol. 15(20), pages 1-15, October.
    18. Jin, Jiahuan & Ma, Mingyu & Jin, Huan & Cui, Tianxiang & Bai, Ruibin, 2023. "Container terminal daily gate in and gate out forecasting using machine learning methods," Transport Policy, Elsevier, vol. 132(C), pages 163-174.

  7. Busola Oluwafemi, Tolulope & Mitchelmore, Siwan & Nikolopoulos, Konstantinos, 2020. "Leading innovation: Empirical evidence for ambidextrous leadership from UK high-tech SMEs," Journal of Business Research, Elsevier, vol. 119(C), pages 195-208.

    Cited by:

    1. Gongli Luo & Guangming Zhu & Yanlu Guo, 2023. "Effect of paradoxical leadership on employee innovation behavior in a Confucian context," Asian Business & Management, Palgrave Macmillan, vol. 22(5), pages 2249-2279, November.
    2. Chukiat Siriwong & Siwarit Pongsakornrungsilp & Pimlapas Pongsakornrungsilp & Vikas Kumar, 2024. "Mapping the Terrain of Open Innovation in Consumer Research: Insights and Directions from Bibliometrics," Sustainability, MDPI, vol. 16(15), pages 1-22, July.
    3. Mohammad Taghi Taghavifard & Setareh Majidian, 2022. "Identifying Cloud Computing Risks based on Firm’s Ambidexterity Performance using Fuzzy VIKOR Technique," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 113-133, March.
    4. Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Rodríguez-Espíndola, Oscar & Parkes, Geoff & Tuyet, Nguyen Thi Anh & Long, Dang Duc & Ha, Tran Phuong, 2022. "Impact of Organisational Factors on the Circular Economy Practices and Sustainable Performance of Small and Medium-sized Enterprises in Vietnam," Journal of Business Research, Elsevier, vol. 147(C), pages 362-378.
    5. Mihaela Simionescu & Cristinel Vasiliu & Corina-Georgiana Serban (Patrintas) & Andreea-Nicoleta Bichel & Oana Simona Hudea, 2023. "Towards a Modern Leadership: Sustainable Development-Oriented Management," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 25(S17), pages 1024-1024, November.
    6. Pedersen, Carsten Lund, 2021. "Taking matters into one’s own hands? Addressing the relational nature of FLE autonomy," Journal of Business Research, Elsevier, vol. 136(C), pages 366-376.
    7. Corina Georgiana ?ERBAN (PATRINTAS) & Andreea-Nicoleta BICHEL & Denisa ?ARANU & Drago? BUJOR, 2023. "Covid-19 Crisis Impact on Leadership: Innovation, Digitalization and CSR Dimensions," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 24(1), pages 78-91, March.
    8. Shuxin Zhang & Sid Suntrayuth, 2024. "The Synergy of Ambidextrous Leadership, Agility, and Entrepreneurial Orientation to Achieve Sustainable AI Product Innovation," Sustainability, MDPI, vol. 16(10), pages 1-20, May.
    9. Rabab H. Saleh & Christopher M. Durugbo & Soud M. Almahamid, 2023. "What makes innovation ambidexterity manageable: a systematic review, multi-level model and future challenges," Review of Managerial Science, Springer, vol. 17(8), pages 3013-3056, November.
    10. Gökhan Akıncı & Lutfihak Alpkan & Bora Yıldız & Gaye Karacay, 2022. "The Link between Ambidextrous Leadership and Innovative Work Behavior in a Military Organization: The Moderating Role of Climate for Innovation," Sustainability, MDPI, vol. 14(22), pages 1-19, November.

  8. Bagheri, Mahshid & Mitchelmore, Siwan & Bamiatzi, Vassiliki & Nikolopoulos, Konstantinos, 2019. "Internationalization Orientation in SMEs: The Mediating Role of Technological Innovation," Journal of International Management, Elsevier, vol. 25(1), pages 121-139.

    Cited by:

    1. Miocevic, Dario, 2021. "Dynamic exporting capabilities and SME’s profitability: Conditional effects of market and product diversification," Journal of Business Research, Elsevier, vol. 136(C), pages 21-32.
    2. Ko, Dr. Wai Wai & Chen, Prof. Yantai & Chen, Dr. Cheng-Hao Steve & Wu, Dr. Meng-Shan Sharon & Liu, Prof. Gordon, 2021. "Proactive Environmental Strategy, Foreign Institutional Pressures, and Internationalization of Chinese SMEs," Journal of World Business, Elsevier, vol. 56(6).
    3. Bárbara Ilze Semensato & Fábio Lotti Oliva & Gilles Roehrich, 2022. "Innovation as an internationalisation determinant of Brazilian technology-based SMEs [La Innovación como Determinante de la Internacionalización de las PYMEs Brasileñas de Base Tecnológica]," Journal of International Entrepreneurship, Springer, vol. 20(3), pages 404-432, September.
    4. Castilla-Polo, Francisca & Sánchez-Hernández, M. Isabel, 2022. "International orientation: An antecedent-consequence model in Spanish agri-food cooperatives which are aware of the circular economy," Journal of Business Research, Elsevier, vol. 152(C), pages 231-241.
    5. Kang, Yuanfei & Scott-Kennel, Joanna & Battisti, Martina & Deakins, David, 2021. "Linking inward/outward FDI and exploitation/exploration strategies: Development of a framework for SMEs," International Business Review, Elsevier, vol. 30(3).
    6. Totok Sasongko & Muhamad Rifa'i & Nugraheni Suci Sayekti, 2018. "The Development of the Creative Industries to Create a Competitive Advantage: Studies in Small Business Sector," Journal of Economic Development, Environment and People, Alliance of Central-Eastern European Universities, vol. 7(3), pages 14-23, September.
    7. Al-Tabbaa, Omar & Zahoor, Nadia, 2024. "Alliance management capability and SMEs’ international expansion: The role of innovation pathways," Journal of Business Research, Elsevier, vol. 171(C).
    8. Li, Xiaoxuan & Wang, Yue & Yang, Miles M. & Tang, Yanzhao, 2022. "Does owner CEO narcissism promote exporting SMEs' market spreading strategy? Joint effects of asset-specific investments and firm exporting experience," Journal of International Management, Elsevier, vol. 28(3).
    9. Lee, Jeoung Yul & Yang, Young Soo & Ghauri, Pervez N. & Park, Byung Il, 2022. "The Impact of Social Media and Digital Platforms Experience on SME International Orientation: The Moderating Role of COVID-19 Pandemic," Journal of International Management, Elsevier, vol. 28(4).
    10. Cagri Bulut & Secil Pelin Aka & Murat Nazli, 2021. "Strategic orientations toward technological innovativeness in the marble industry," SN Business & Economics, Springer, vol. 1(10), pages 1-14, October.
    11. Costantiello, Alberto & Laureti, Lucio & De Cristoforo, Gianluca & Leogrande, Angelo, 2021. "The Innovation-Sales Growth Nexus in Europe," MPRA Paper 106858, University Library of Munich, Germany, revised 28 Mar 2021.
    12. Crespo, Nuno Fernandes & Crespo, Cátia Fernandes & Silva, Graça Miranda, 2024. "Every cloud has a silver lining: The role of business digitalization and early internationalization strategies to overcome cloudy times," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    13. Deng, Wei & Hubner-Benz, Sylvia & Frese, Michael & Song, Zhaoli, 2023. "Different ways lead to ambidexterity: Configurations for team innovation across China, India, and Singapore," Journal of International Management, Elsevier, vol. 29(3).
    14. Fariborzi, Hadi & Osiyevskyy, Oleksiy & DaSilva, Carlos, 2022. "The effect of geographic scope on growth and growth variability of SMEs," Journal of World Business, Elsevier, vol. 57(5).
    15. Eduardsen, Jonas & Marinova, Svetla Trifonova & González-Loureiro, Miguel & Vlačić, Božidar, 2022. "Business group affiliation and SMEs’ international sales intensity and diversification: A multi-country study," International Business Review, Elsevier, vol. 31(5).
    16. Dung Nguyen‐Van & Chia‐Hua Chang, 2021. "Internationalization and product innovation in ASEAN: The moderating role of organizational innovation," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(2), pages 437-462, March.
    17. Bing Feng & Kaiyang Sun & Min Chen & Tao Gao, 2020. "The Impact of Core Technological Capabilities of High-Tech Industry on Sustainable Competitive Advantage," Sustainability, MDPI, vol. 12(7), pages 1-15, April.
    18. Zahoor, Nadia & Donbesuur, Francis & Christofi, Michael & Miri, Domnan, 2022. "Technological innovation and employee psychological well-being: The moderating role of employee learning orientation and perceived organizational support," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    19. Feliciano-Cestero, María M. & Ameen, Nisreen & Kotabe, Masaaki & Paul, Justin & Signoret, Mario, 2023. "Is digital transformation threatened? A systematic literature review of the factors influencing firms’ digital transformation and internationalization," Journal of Business Research, Elsevier, vol. 157(C).
    20. Muhammad Talha Khan & Muhammad Dawood Idrees & Muhammad Rauf & Abdul Sami & Arsalan Ansari & Atif Jamil, 2022. "Green Supply Chain Management Practices’ Impact on Operational Performance with the Mediation of Technological Innovation," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    21. Swati Agrawal & Poonam Singh & Mainak Mazumdar, 2021. "Innovation, Firm Size and Ownership: A Study of Firm Transition in India," International Journal of Global Business and Competitiveness, Springer, vol. 16(1), pages 15-27, June.
    22. Younis, Heba & Elbanna, Said, 2022. "How Do SMEs Decide on International Market Entry? An Empirical Examination in the Middle East," Journal of International Management, Elsevier, vol. 28(1).
    23. Daniel Sommer & Bernd Ebersberger, 2021. "International R&D teams: Performance effects and the moderating role of technological competences," Economics Bulletin, AccessEcon, vol. 41(2), pages 387-397.
    24. Akter, Mansura & Akter, Shahriar & Rahman, Mahfuzur & Priporas, Constantinos Vasilios, 2023. "Mapping the barriers to socio-economic freedom in internationalisation of women-owned SMEs: Evidence from a developing country," Journal of International Management, Elsevier, vol. 29(6).
    25. Alberto Bertello & Alberto Ferraris & Stefano Bresciani & Paola Bernardi, 2021. "Big data analytics (BDA) and degree of internationalization: the interplay between governance of BDA infrastructure and BDA capabilities," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 25(4), pages 1035-1055, December.
    26. Wu, Shang-Ho & Lin, Feng-Jyh & Perng, Chyuan, 2022. "The affecting factors of small and medium enterprise performance 11The authors thank Kunhuang Huarng, National taipei University of Bussiness, and Fang-Yi Lo, Feng Chia University for their careful re," Journal of Business Research, Elsevier, vol. 143(C), pages 94-104.
    27. Galindo-Martín, Miguel-Ángel & Castaño-Martínez, María-Soledad & Méndez-Picazo, María-Teresa, 2021. "The role of entrepreneurship in different economic phases," Journal of Business Research, Elsevier, vol. 122(C), pages 171-179.
    28. Osarumwense Osabuohien-Irabor & Igor Mikhailovich Drapkin, 2022. "The Impact of Technological Innovation on Energy Consumption in OECD Economies: the role of Outward Foreign Direct Investment and International Trade Openness," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 317-333, July.
    29. Sikandar Ali Qalati & Wenyuan Li & Naveed Ahmed & Manzoor Ali Mirani & Asadullah Khan, 2020. "Examining the Factors Affecting SME Performance: The Mediating Role of Social Media Adoption," Sustainability, MDPI, vol. 13(1), pages 1-24, December.
    30. Nadia Zahoor & Yong Kyu Lew, 2022. "Sustaining superior international performance: Strategic orientations and dynamic capability of environmentally concerned small‐ and medium‐sized enterprises," Business Strategy and the Environment, Wiley Blackwell, vol. 31(3), pages 1002-1017, March.
    31. Juergensen, Jill Josefina & Narula, Rajneesh & Surdu, Irina, 2022. "A systematic review of the relationship between international diversification and innovation: A firm-level perspective," International Business Review, Elsevier, vol. 31(2).
    32. Sommer, Daniel & Bhandari, Krishna Raj, 2022. "Internationalization of R&D and Innovation Performance in the Pharma Industry," Journal of International Management, Elsevier, vol. 28(3).
    33. Han Xiao & Abdullah Al Mamun & Mohammad Masukujjaman & Qing Yang, 2023. "Modelling the significance of strategic orientation on green innovation: mediation of green dynamic capabilities," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
    34. Crespo, Nuno Fernandes & Crespo, Cátia Fernandes & Silva, Graça Miranda & Nicola, Maura Bedin, 2023. "Innovation in times of crisis: The relevance of digitalization and early internationalization strategies," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    35. Idris, Bochra & Saridakis, George & Khan, Zaheer, 2022. "The Effect of Outward and Inward Internationalisation on Different Types of Innovation: Evidence from UK SMEs," Journal of International Management, Elsevier, vol. 28(2).
    36. Luis Javier Garcia-Martinez & Sascha Kraus & Matthias Breier & Andreas Kallmuenzer, 2023. "Untangling the relationship between small and medium-sized enterprises and growth: a review of extant literature," International Entrepreneurship and Management Journal, Springer, vol. 19(2), pages 455-479, June.

  9. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.

    Cited by:

    1. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
    3. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Generalizing the Theta method for automatic forecasting," European Journal of Operational Research, Elsevier, vol. 284(2), pages 550-558.
    4. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    5. Luo, Jian & Hong, Tao & Gao, Zheming & Fang, Shu-Cherng, 2023. "A robust support vector regression model for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 1005-1020.

  10. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2019. "Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors," International Journal of Production Economics, Elsevier, vol. 209(C), pages 92-102.

    Cited by:

    1. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    2. Sbrana, Giacomo & Silvestrini, Andrea, 2020. "Forecasting with the damped trend model using the structural approach," International Journal of Production Economics, Elsevier, vol. 226(C).
    3. Jaganathan, Srihari & Prakash, P.K.S., 2020. "A combination-based forecasting method for the M4-competition," International Journal of Forecasting, Elsevier, vol. 36(1), pages 98-104.
    4. Sbrana, Giacomo & Silvestrini, Andrea, 2023. "The RWDAR model: A novel state-space approach to forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 922-937.
    5. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
    7. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Generalizing the Theta method for automatic forecasting," European Journal of Operational Research, Elsevier, vol. 284(2), pages 550-558.
    8. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).

  11. Phillips, Christina Jane & Nikolopoulos, Konstantinos, 2019. "Forecast quality improvement with Action Research: A success story at PharmaCo," International Journal of Forecasting, Elsevier, vol. 35(1), pages 129-143.

    Cited by:

    1. Fildes, Robert & Goodwin, Paul, 2021. "Stability in the inefficient use of forecasting systems: A case study in a supply chain company," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1031-1046.

  12. Fotios Petropoulos & Konstantinos Nikolopoulos, 2017. "The Theta Method," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 46, pages 11-17, Summer.

    Cited by:

    1. Sbrana, Giacomo & Silvestrini, Andrea, 2023. "The RWDAR model: A novel state-space approach to forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 922-937.
    2. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.

  13. Petropoulos, Fotios & Kourentzes, Nikolaos & Nikolopoulos, Konstantinos, 2016. "Another look at estimators for intermittent demand," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 154-161.

    Cited by:

    1. Ducharme, Corey & Agard, Bruno & Trépanier, Martin, 2021. "Forecasting a customer's Next Time Under Safety Stock," International Journal of Production Economics, Elsevier, vol. 234(C).
    2. Petropoulos, Fotios & Wang, Xun & Disney, Stephen M., 2019. "The inventory performance of forecasting methods: Evidence from the M3 competition data," International Journal of Forecasting, Elsevier, vol. 35(1), pages 251-265.
    3. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    4. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    5. Murray, Paul W. & Agard, Bruno & Barajas, Marco A., 2018. "ASACT - Data preparation for forecasting: A method to substitute transaction data for unavailable product consumption data," International Journal of Production Economics, Elsevier, vol. 203(C), pages 264-275.
    6. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.
    7. Sroginis, Anna & Fildes, Robert & Kourentzes, Nikolaos, 2023. "Use of contextual and model-based information in adjusting promotional forecasts," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1177-1191.
    8. Jeon, Jooyoung & Panagiotelis, Anastasios & Petropoulos, Fotios, 2019. "Probabilistic forecast reconciliation with applications to wind power and electric load," European Journal of Operational Research, Elsevier, vol. 279(2), pages 364-379.
    9. Tian, Xin & Wang, Haoqing & E, Erjiang, 2021. "Forecasting intermittent demand for inventory management by retailers: A new approach," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    10. Jože Martin Rožanec & Blaž Fortuna & Dunja Mladenić, 2022. "Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    11. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    12. Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.
    13. Huber, Jakob & Stuckenschmidt, Heiner, 2021. "Intraday shelf replenishment decision support for perishable goods," International Journal of Production Economics, Elsevier, vol. 231(C).

  14. Nikolopoulos, Konstantinos I. & Babai, M. Zied & Bozos, Konstantinos, 2016. "Forecasting supply chain sporadic demand with nearest neighbor approaches," International Journal of Production Economics, Elsevier, vol. 177(C), pages 139-148.

    Cited by:

    1. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    2. Evangelos Spiliotis & Spyros Makridakis & Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos, 2022. "Comparison of statistical and machine learning methods for daily SKU demand forecasting," Operational Research, Springer, vol. 22(3), pages 3037-3061, July.
    3. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    4. Uddin, Gazi Salah & Tang, Ou & Sahamkhadam, Maziar & Taghizadeh-Hesary, Farhad & Yahya, Muhammad & Cerin, Pontus & Rehme, Jakob, 2021. "Analysis of Forecasting Models in an Electricity Market under Volatility," ADBI Working Papers 1212, Asian Development Bank Institute.
    5. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    6. G. Peter Zhang & Yusen Xia & Maohua Xie, 2024. "Intermittent demand forecasting with transformer neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 1051-1072, August.
    7. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    8. Corey Ducharme & Bruno Agard & Martin Trépanier, 2024. "Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1661-1681, August.
    9. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
    10. Keun Hee Lee & Mali Abdollahian & Sergei Schreider & Sona Taheri, 2023. "Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect," Mathematics, MDPI, vol. 11(11), pages 1-28, May.
    11. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    12. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    13. Hasni, M. & Aguir, M.S. & Babai, M.Z. & Jemai, Z., 2019. "On the performance of adjusted bootstrapping methods for intermittent demand forecasting," International Journal of Production Economics, Elsevier, vol. 216(C), pages 145-153.
    14. Silva, Allyson & Roodbergen, Kees Jan & Coelho, Leandro C. & Darvish, Maryam, 2022. "Estimating optimal ABC zone sizes in manual warehouses," International Journal of Production Economics, Elsevier, vol. 252(C).

  15. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.

    Cited by:

    1. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    2. Ducharme, Corey & Agard, Bruno & Trépanier, Martin, 2021. "Forecasting a customer's Next Time Under Safety Stock," International Journal of Production Economics, Elsevier, vol. 234(C).
    3. Lessmann, Stefan & Voß, Stefan, 2017. "Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy," International Journal of Forecasting, Elsevier, vol. 33(4), pages 864-877.
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    26. Katsagounos, Ilias & Thomakos, Dimitrios D. & Litsiou, Konstantia & Nikolopoulos, Konstantinos, 2021. "Superforecasting reality check: Evidence from a small pool of experts and expedited identification," European Journal of Operational Research, Elsevier, vol. 289(1), pages 107-117.
    27. Ma, Shaohui & Fildes, Robert, 2022. "The performance of the global bottom-up approach in the M5 accuracy competition: A robustness check," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1492-1499.
    28. Büttner, Daniel & Scheidler, Anne Antonia & Rabe, Markus, 2021. "A reference model for data-driven sales planning: Development of the model's framework and functionality," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 441-476, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    29. Man Yang & Tao Zhang, 2023. "Demand forecasting and information sharing of a green supply chain considering data company," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-28, July.
    30. Li, Han & Li, Hong & Lu, Yang & Panagiotelis, Anastasios, 2019. "A forecast reconciliation approach to cause-of-death mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 122-133.
    31. Jonathan Gumz & Diego Castro Fettermann & Enzo Morosini Frazzon & Mirko Kück, 2022. "Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions," Sustainability, MDPI, vol. 14(20), pages 1-34, October.
    32. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2022. "Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems," International Journal of Forecasting, Elsevier, vol. 38(1), pages 178-192.
    33. Zhou, Maosen & Dan, Bin & Ma, Songxuan & Zhang, Xumei, 2017. "Supply chain coordination with information sharing: The informational advantage of GPOs," European Journal of Operational Research, Elsevier, vol. 256(3), pages 785-802.
    34. Lobo, Benjamin J. & Brown, Donald E. & Grazaitis, Peter J., 2019. "Long-term forecasting of fuel demand at theater entry points," International Journal of Forecasting, Elsevier, vol. 35(2), pages 502-520.
    35. Zhang, Bohan & Panagiotelis, Anastasios & Kang, Yanfei, 2024. "Discrete forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 318(1), pages 143-153.
    36. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    37. Sroginis, Anna & Fildes, Robert & Kourentzes, Nikolaos, 2023. "Use of contextual and model-based information in adjusting promotional forecasts," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1177-1191.
    38. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
    39. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    40. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    41. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    42. Yihang Zhu & Yinglei Zhao & Jingjin Zhang & Na Geng & Danfeng Huang, 2019. "Spring onion seed demand forecasting using a hybrid Holt-Winters and support vector machine model," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-18, July.
    43. Joanna Bruzda, 2020. "Multistep quantile forecasts for supply chain and logistics operations: bootstrapping, the GARCH model and quantile regression based approaches," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 309-336, March.
    44. Shubhra Paul & Lauren B. Davis, 2022. "An ensemble forecasting model for predicting contribution of food donors based on supply behavior," Annals of Operations Research, Springer, vol. 319(1), pages 1-29, December.
    45. Dominguez, Roberto & Cannella, Salvatore & Framinan, Jose M., 2021. "Remanufacturing configuration in complex supply chains," Omega, Elsevier, vol. 101(C).
    46. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    47. Jože Martin Rožanec & Blaž Fortuna & Dunja Mladenić, 2022. "Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    48. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    49. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    50. Villegas, Marco A. & Pedregal, Diego J., 2019. "Automatic selection of unobserved components models for supply chain forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 157-169.
    51. Sun, Zhengwei & Hupman, Andrea C. & Abbas, Ali E., 2021. "The value of information for price dependent demand," European Journal of Operational Research, Elsevier, vol. 288(2), pages 511-522.
    52. Li, Han & Hyndman, Rob J., 2021. "Assessing mortality inequality in the U.S.: What can be said about the future?," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 152-162.
    53. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Generalizing the Theta method for automatic forecasting," European Journal of Operational Research, Elsevier, vol. 284(2), pages 550-558.
    54. Hakeem‐Ur Rehman & Guohua Wan & Raza Rafique, 2023. "A hybrid approach with step‐size aggregation to forecasting hierarchical time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 176-192, January.
    55. Dominguez, Roberto & Cannella, Salvatore & Barbosa-Póvoa, Ana P. & Framinan, Jose M., 2018. "OVAP: A strategy to implement partial information sharing among supply chain retailers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 122-136.
    56. Liu, Congzheng & Letchford, Adam N. & Svetunkov, Ivan, 2022. "Newsvendor problems: An integrated method for estimation and optimisation," European Journal of Operational Research, Elsevier, vol. 300(2), pages 590-601.
    57. Tine Van Calster & Filip Van den Bossche & Bart Baesens & Wilfried Lemahieu, 2020. "Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective," Papers 2002.00949, arXiv.org.
    58. Omar, Haytham & Klibi, Walid & Babai, M. Zied & Ducq, Yves, 2023. "Basket data-driven approach for omnichannel demand forecasting," International Journal of Production Economics, Elsevier, vol. 257(C).
    59. Kim, Nayeon & Montreuil, Benoit & Klibi, Walid, 2022. "Inventory availability commitment under uncertainty in a dropshipping supply chain," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1155-1174.
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    61. Maud van den Broeke & Shari de Baets & Ann Vereecke & Philippe Baecke & Karlien Vanderheyden, 2019. "Judgmental forecast adjustments over different time horizons," Post-Print hal-03001747, HAL.
    62. Aljuneidi, Tariq & Punia, Sushil & Jebali, Aida & Nikolopoulos, Konstantinos, 2024. "Forecasting and planning for a critical infrastructure sector during a pandemic: Empirical evidence from a food supply chain," European Journal of Operational Research, Elsevier, vol. 317(3), pages 936-952.
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  16. Nikolopoulos, Konstantinos & Buxton, Samantha & Khammash, Marwan & Stern, Philip, 2016. "Forecasting branded and generic pharmaceuticals," International Journal of Forecasting, Elsevier, vol. 32(2), pages 344-357.

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    1. Naoum Tsolakis & Jagjit Singh Srai, 2018. "Mapping supply dynamics in renewable feedstock enabled industries: A systems theory perspective on ‘green’ pharmaceuticals," Operations Management Research, Springer, vol. 11(3), pages 83-104, December.
    2. Lu, Emiao & Handl, Julia & Xu, Dong-ling, 2018. "Determining analogies based on the integration of multiple information sources," International Journal of Forecasting, Elsevier, vol. 34(3), pages 507-528.
    3. Yong-Jae Lee & Young Jae Han & Sang-Soo Kim & Chulung Lee, 2022. "Patent Data Analytics for Technology Forecasting of the Railway Main Transformer," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    4. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    5. Alptekin Durmuşoğlu, 2017. "Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences," Sustainability, MDPI, vol. 9(5), pages 1-10, May.
    6. Xiaodan Zhu & Anh Ninh & Hui Zhao & Zhenming Liu, 2021. "Demand Forecasting with Supply‐Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3231-3252, September.
    7. Bo Tan & Zhiguo Zhu & Pan Jiang & Xiening Wang, 2023. "Modeling Multi-Generation Product Diffusion in the Context of Dual-Brand Competition and Sustainable Improvement," Sustainability, MDPI, vol. 15(17), pages 1-22, August.
    8. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.

  17. Dimitrios D. Thomakos & Konstantinos Nikolopoulos, 2015. "Forecasting Multivariate Time Series with the Theta Method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 220-229, April.
    See citations under working paper version above.
  18. Nikolopoulos, Konstantinos & Litsa, Akrivi & Petropoulos, Fotios & Bougioukos, Vasileios & Khammash, Marwan, 2015. "Relative performance of methods for forecasting special events," Journal of Business Research, Elsevier, vol. 68(8), pages 1785-1791.

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    1. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    2. Lu, Emiao & Handl, Julia & Xu, Dong-ling, 2018. "Determining analogies based on the integration of multiple information sources," International Journal of Forecasting, Elsevier, vol. 34(3), pages 507-528.
    3. Abolghasemi, Mahdi & Tarr, Garth & Bergmeir, Christoph, 2024. "Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 597-615.
    4. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    5. Konstantinos Nikolopoulos & Waleed S. Alghassab & Konstantia Litsiou & Stelios Sapountzis, 2019. "Long-Term Economic Forecasting with Structured Analogies and Interaction Groups," Working Papers 19018, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    6. Katsagounos, Ilias & Thomakos, Dimitrios D. & Litsiou, Konstantia & Nikolopoulos, Konstantinos, 2021. "Superforecasting reality check: Evidence from a small pool of experts and expedited identification," European Journal of Operational Research, Elsevier, vol. 289(1), pages 107-117.
    7. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    8. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    9. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
    10. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    11. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.

  19. Spithourakis, Georgios P. & Petropoulos, Fotios & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2015. "Amplifying the learning effects via a Forecasting and Foresight Support System," International Journal of Forecasting, Elsevier, vol. 31(1), pages 20-32.

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    1. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    2. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    3. Hansen, Mette Sanne & Rasmussen, Lauge Baungaard & Jacobsen, Peter, 2016. "Interactive foresight simulation," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 214-227.

  20. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.

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    1. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    2. Petropoulos, Fotios & Kourentzes, Nikolaos & Nikolopoulos, Konstantinos, 2016. "Another look at estimators for intermittent demand," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 154-161.
    3. Mahsa Pahlevani & Majid Taghavi & Peter Vanberkel, 2024. "A systematic literature review of predicting patient discharges using statistical methods and machine learning," Health Care Management Science, Springer, vol. 27(3), pages 458-478, September.
    4. Ye, Yuan & Lu, Yonggang & Robinson, Powell & Narayanan, Arunachalam, 2022. "An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control," European Journal of Operational Research, Elsevier, vol. 303(1), pages 255-272.
    5. Wesley Marcos Almeida & Claudimar Pereira Veiga, 2023. "Does demand forecasting matter to retailing?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 219-232, June.
    6. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    7. Lu, Emiao & Handl, Julia & Xu, Dong-ling, 2018. "Determining analogies based on the integration of multiple information sources," International Journal of Forecasting, Elsevier, vol. 34(3), pages 507-528.
    8. Evangelos Spiliotis & Spyros Makridakis & Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos, 2022. "Comparison of statistical and machine learning methods for daily SKU demand forecasting," Operational Research, Springer, vol. 22(3), pages 3037-3061, July.
    9. Price, Ilan & Fowkes, Jaroslav & Hopman, Daniel, 2019. "Gaussian processes for unconstraining demand," European Journal of Operational Research, Elsevier, vol. 275(2), pages 621-634.
    10. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    11. Spiliotis, Evangelos & Kouloumos, Andreas & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Are forecasting competitions data representative of the reality?," International Journal of Forecasting, Elsevier, vol. 36(1), pages 37-53.
    12. Kubiv Stepan & Balanyuk Yuriy, 2019. "Development of a combined method for predicting discrete time series with non-stability for forecasting military goods demand," Technology audit and production reserves, Socionet;Technology audit and production reserves, vol. 6(4(50)).
    13. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    14. Kang, Yanfei & Cao, Wei & Petropoulos, Fotios & Li, Feng, 2022. "Forecast with forecasts: Diversity matters," European Journal of Operational Research, Elsevier, vol. 301(1), pages 180-190.
    15. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    16. Veiga, Claudimar Pereira da & Veiga, Cássia Rita Pereira da & Puchalski, Weslly & Coelho, Leandro dos Santos & Tortato, Ubiratã, 2016. "Demand forecasting based on natural computing approaches applied to the foodstuff retail segment," Journal of Retailing and Consumer Services, Elsevier, vol. 31(C), pages 174-181.
    17. Bacci, Livio Agnew & Mello, Luiz Gustavo & Incerti, Taynara & Paulo de Paiva, Anderson & Balestrassi, Pedro Paulo, 2019. "Optimization of combined time series methods to forecast the demand for coffee in Brazil: A new approach using Normal Boundary Intersection coupled with mixture designs of experiments and rotated fact," International Journal of Production Economics, Elsevier, vol. 212(C), pages 186-211.
    18. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
    19. Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.
    20. Nikolopoulos, Konstantinos & Buxton, Samantha & Khammash, Marwan & Stern, Philip, 2016. "Forecasting branded and generic pharmaceuticals," International Journal of Forecasting, Elsevier, vol. 32(2), pages 344-357.
    21. Petropoulos, Fotios & Hyndman, Rob J. & Bergmeir, Christoph, 2018. "Exploring the sources of uncertainty: Why does bagging for time series forecasting work?," European Journal of Operational Research, Elsevier, vol. 268(2), pages 545-554.
    22. Mareček, Jakub & Richtárik, Peter & Takáč, Martin, 2017. "Matrix completion under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 256(1), pages 35-43.
    23. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
    24. Turrini, Laura & Meissner, Joern, 2019. "Spare parts inventory management: New evidence from distribution fitting," European Journal of Operational Research, Elsevier, vol. 273(1), pages 118-130.
    25. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2018. "The M4 Competition: Results, findings, conclusion and way forward," International Journal of Forecasting, Elsevier, vol. 34(4), pages 802-808.
    26. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Generalizing the Theta method for automatic forecasting," European Journal of Operational Research, Elsevier, vol. 284(2), pages 550-558.
    27. Kang, Yanfei & Hyndman, Rob J. & Smith-Miles, Kate, 2017. "Visualising forecasting algorithm performance using time series instance spaces," International Journal of Forecasting, Elsevier, vol. 33(2), pages 345-358.
    28. Petropoulos, Fotios & Fildes, Robert & Goodwin, Paul, 2016. "Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 842-852.
    29. Tine Van Calster & Filip Van den Bossche & Bart Baesens & Wilfried Lemahieu, 2020. "Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective," Papers 2002.00949, arXiv.org.
    30. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    31. Fiorucci, Jose A. & Pellegrini, Tiago R. & Louzada, Francisco & Petropoulos, Fotios & Koehler, Anne B., 2016. "Models for optimising the theta method and their relationship to state space models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1151-1161.
    32. Kostas Nikolopoulos & F. Petropoulos, 2015. "Forecasting, Foresight and Strategic Planning for Black Swans," Working Papers 15003, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    33. Rennie, Nicola & Cleophas, Catherine & Sykulski, Adam M. & Dost, Florian, 2021. "Identifying and responding to outlier demand in revenue management," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1015-1030.
    34. Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.
    35. Nguyen, Duy Tan & Adulyasak, Yossiri & Landry, Sylvain, 2021. "Research manuscript: The Bullwhip Effect in rule-based supply chain planning systems–A case-based simulation at a hard goods retailer," Omega, Elsevier, vol. 98(C).
    36. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.
    37. Boutselis, Petros & McNaught, Ken, 2019. "Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context," International Journal of Production Economics, Elsevier, vol. 209(C), pages 325-333.
    38. Nikolopoulos, Konstantinos I. & Babai, M. Zied & Bozos, Konstantinos, 2016. "Forecasting supply chain sporadic demand with nearest neighbor approaches," International Journal of Production Economics, Elsevier, vol. 177(C), pages 139-148.

  21. Savio, Nicolas D. & Nikolopoulos, Konstantinos, 2013. "A strategic forecasting framework for governmental decision-making and planning," International Journal of Forecasting, Elsevier, vol. 29(2), pages 311-321.

    Cited by:

    1. Lu, Emiao & Handl, Julia & Xu, Dong-ling, 2018. "Determining analogies based on the integration of multiple information sources," International Journal of Forecasting, Elsevier, vol. 34(3), pages 507-528.
    2. Katsagounos, Ilias & Thomakos, Dimitrios D. & Litsiou, Konstantia & Nikolopoulos, Konstantinos, 2021. "Superforecasting reality check: Evidence from a small pool of experts and expedited identification," European Journal of Operational Research, Elsevier, vol. 289(1), pages 107-117.
    3. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.
    4. Nikolopoulos, Konstantinos & Litsa, Akrivi & Petropoulos, Fotios & Bougioukos, Vasileios & Khammash, Marwan, 2015. "Relative performance of methods for forecasting special events," Journal of Business Research, Elsevier, vol. 68(8), pages 1785-1791.

  22. Akrivi LITSA & Fotios PETROPOULOS & Konstantinos NIKOLOPOULOS, 2012. "Forecasting the Success of Governmental "Incentivized" Initiatives: Case Study of a New Policy Promoting the Replacement of Old Household; Air-conditioners," Journal of Knowledge Management, Economics and Information Technology, ScientificPapers.org, vol. 2(1), pages 1-15, February.

    Cited by:

    1. Katsagounos, Ilias & Thomakos, Dimitrios D. & Litsiou, Konstantia & Nikolopoulos, Konstantinos, 2021. "Superforecasting reality check: Evidence from a small pool of experts and expedited identification," European Journal of Operational Research, Elsevier, vol. 289(1), pages 107-117.

  23. Babai, M. Zied & Ali, Mohammad M. & Nikolopoulos, Konstantinos, 2012. "Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis," Omega, Elsevier, vol. 40(6), pages 713-721.

    Cited by:

    1. Prak, Dennis & Teunter, Ruud & Babai, Mohamed Zied & Boylan, John E. & Syntetos, Aris, 2021. "Robust compound Poisson parameter estimation for inventory control," Omega, Elsevier, vol. 104(C).
    2. Petropoulos, Fotios & Wang, Xun & Disney, Stephen M., 2019. "The inventory performance of forecasting methods: Evidence from the M3 competition data," International Journal of Forecasting, Elsevier, vol. 35(1), pages 251-265.
    3. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    4. Rostami-Tabar, Bahman & Babai, M. Zied & Ali, Mohammad & Boylan, John E., 2019. "The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes," European Journal of Operational Research, Elsevier, vol. 273(3), pages 920-932.
    5. Boylan, John E. & Babai, M. Zied, 2016. "On the performance of overlapping and non-overlapping temporal demand aggregation approaches," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 136-144.
    6. Nikolaos Kourentzes & Dong Li & Arne K. Strauss, 2019. "Unconstraining methods for revenue management systems under small demand," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(1), pages 27-41, February.
    7. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    8. Babai, M. Zied & Dai, Yong & Li, Qinyun & Syntetos, Aris & Wang, Xun, 2022. "Forecasting of lead-time demand variance: Implications for safety stock calculations," European Journal of Operational Research, Elsevier, vol. 296(3), pages 846-861.
    9. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
    10. Aiping Jiang & Kwok Leung Tam & Xiaoyun Guo & Yufeng Zhang, 2020. "A new approach to forecasting intermittent demand based on the mixed zero‐truncated Poisson model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 69-83, January.
    11. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    12. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    13. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    14. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    15. Che-Yu Hung & Chien-Chih Wang & Shi-Woei Lin & Bernard C. Jiang, 2022. "An Empirical Comparison of the Sales Forecasting Performance for Plastic Tray Manufacturing Using Missing Data," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    16. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    17. Tian, Xin & Wang, Haoqing & E, Erjiang, 2021. "Forecasting intermittent demand for inventory management by retailers: A new approach," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    18. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
    19. Turrini, Laura & Meissner, Joern, 2019. "Spare parts inventory management: New evidence from distribution fitting," European Journal of Operational Research, Elsevier, vol. 273(1), pages 118-130.
    20. Bahman Rostami‐Tabar & M. Zied Babai & Aris Syntetos & Yves Ducq, 2013. "Demand forecasting by temporal aggregation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(6), pages 479-498, September.
    21. Van der Auweraer, Sarah & Boute, Robert, 2019. "Forecasting spare part demand using service maintenance information," International Journal of Production Economics, Elsevier, vol. 213(C), pages 138-149.
    22. Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.
    23. Nikolopoulos, Konstantinos I. & Babai, M. Zied & Bozos, Konstantinos, 2016. "Forecasting supply chain sporadic demand with nearest neighbor approaches," International Journal of Production Economics, Elsevier, vol. 177(C), pages 139-148.

  24. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660, July.

    Cited by:

    1. Ali Caner Türkmen & Tim Januschowski & Yuyang Wang & Ali Taylan Cemgil, 2021. "Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-26, November.
    2. Lessmann, Stefan & Voß, Stefan, 2017. "Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy," International Journal of Forecasting, Elsevier, vol. 33(4), pages 864-877.
    3. Ioannis Nasios & Konstantinos Vogklis, 2023. "Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series," Papers 2310.13029, arXiv.org.
    4. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    5. Rob J Hyndman, 2019. "A Brief History of Forecasting Competitions," Monash Econometrics and Business Statistics Working Papers 3/19, Monash University, Department of Econometrics and Business Statistics.
    6. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    7. Nasios, Ioannis & Vogklis, Konstantinos, 2022. "Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1448-1459.
    8. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
    9. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    10. Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers 202210, University of Barcelona, Research Institute of Applied Economics, revised Jul 2022.
    11. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. ""An application of deep learning for exchange rate forecasting"," IREA Working Papers 202201, University of Barcelona, Research Institute of Applied Economics, revised Jan 2022.
    12. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    13. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    14. Alexander Vlasenko & Nataliia Vlasenko & Olena Vynokurova & Dmytro Peleshko, 2018. "A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction," Data, MDPI, vol. 3(4), pages 1-14, December.
    15. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
    16. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    17. Kavitha Ganesan & Udhayakumar Annamalai & Nagarajan Deivanayagampillai, 2019. "An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-19, December.
    18. Bacci, Livio Agnew & Mello, Luiz Gustavo & Incerti, Taynara & Paulo de Paiva, Anderson & Balestrassi, Pedro Paulo, 2019. "Optimization of combined time series methods to forecast the demand for coffee in Brazil: A new approach using Normal Boundary Intersection coupled with mixture designs of experiments and rotated fact," International Journal of Production Economics, Elsevier, vol. 212(C), pages 186-211.
    19. Wellens, Arnoud P. & Boute, Robert N. & Udenio, Maximiliano, 2024. "Simplifying tree-based methods for retail sales forecasting with explanatory variables," European Journal of Operational Research, Elsevier, vol. 314(2), pages 523-539.
    20. Barrow, Devon K. & Crone, Sven F., 2016. "A comparison of AdaBoost algorithms for time series forecast combination," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1103-1119.
    21. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    22. Spyros Makridakis & Chris Fry & Fotios Petropoulos & Evangelos Spiliotis, 2022. "The Future of Forecasting Competitions: Design Attributes and Principles," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 96-113, April.
    23. Ji Wu & Xian Cheng & Stephen Shaoyi Liao, 2020. "Tourism forecast combination using the stochastic frontier analysis technique," Tourism Economics, , vol. 26(7), pages 1086-1107, November.
    24. Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2018. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," HSC Research Reports HSC/18/05, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    25. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    26. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.
    27. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    28. Wellens, Arnoud P. & Udenio, Maxi & Boute, Robert N., 2022. "Transfer learning for hierarchical forecasting: Reducing computational efforts of M5 winning methods," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1482-1491.
    29. Ulrich Gunter & Irem Önder & Egon Smeral, 2020. "Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?," Forecasting, MDPI, vol. 2(3), pages 1-19, June.
    30. Zhidan Luo & Wei Guo & Qingfu Liu & Yiuman Tse, 2023. "A hybrid prediction model with time‐varying gain tracking differentiator in Taylor expansion: Evidence from precious metals," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1138-1149, August.
    31. Bojer, Casper Solheim & Meldgaard, Jens Peder, 2021. "Kaggle forecasting competitions: An overlooked learning opportunity," International Journal of Forecasting, Elsevier, vol. 37(2), pages 587-603.
    32. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    33. Douglas Teodoro & Christian Lovis, 2013. "Empirical Mode Decomposition and k-Nearest Embedding Vectors for Timely Analyses of Antibiotic Resistance Trends," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-14, April.
    34. Huber, Jakob & Müller, Sebastian & Fleischmann, Moritz & Stuckenschmidt, Heiner, 2019. "A data-driven newsvendor problem: From data to decision," European Journal of Operational Research, Elsevier, vol. 278(3), pages 904-915.
    35. Ruhnau, Oliver & Hennig, Patrick & Madlener, Reinhard, 2015. "Economic Implications of Enhanced Forecast Accuracy: The Case of Photovoltaic Feed-In Forecasts," FCN Working Papers 6/2015, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    36. Mergani A. Khairalla & Xu Ning & Nashat T. AL-Jallad & Musaab O. El-Faroug, 2018. "Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model," Energies, MDPI, vol. 11(6), pages 1-21, June.
    37. Dress, Korbinian & Lessmann, Stefan & von Mettenheim, Hans-Jörg, 2018. "Residual value forecasting using asymmetric cost functions," International Journal of Forecasting, Elsevier, vol. 34(4), pages 551-565.
    38. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    39. Tine Van Calster & Filip Van den Bossche & Bart Baesens & Wilfried Lemahieu, 2020. "Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective," Papers 2002.00949, arXiv.org.
    40. Rendon-Sanchez, Juan F. & de Menezes, Lilian M., 2019. "Structural combination of seasonal exponential smoothing forecasts applied to load forecasting," European Journal of Operational Research, Elsevier, vol. 275(3), pages 916-924.
    41. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    42. Erol Eğrioğlu & Robert Fildes, 2022. "A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1355-1383, April.
    43. Barrow, Devon K. & Crone, Sven F., 2016. "Cross-validation aggregation for combining autoregressive neural network forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1120-1137.
    44. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    45. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    46. Ma, Shaohui & Fildes, Robert, 2020. "Forecasting third-party mobile payments with implications for customer flow prediction," International Journal of Forecasting, Elsevier, vol. 36(3), pages 739-760.
    47. Advait Sarkar & Neal Lathia & Cecilia Mascolo, 2015. "Comparing cities’ cycling patterns using online shared bicycle maps," Transportation, Springer, vol. 42(4), pages 541-559, July.
    48. de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
    49. Ruhnau, Oliver & Hennig, Patrick & Madlener, Reinhard, 2020. "Economic implications of forecasting electricity generation from variable renewable energy sources," Renewable Energy, Elsevier, vol. 161(C), pages 1318-1327.

  25. Bozos, Konstantinos & Nikolopoulos, Konstantinos, 2011. "Forecasting the value effect of seasoned equity offering announcements," European Journal of Operational Research, Elsevier, vol. 214(2), pages 418-427, October.

    Cited by:

    1. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.
    2. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    3. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    4. Tine Van Calster & Filip Van den Bossche & Bart Baesens & Wilfried Lemahieu, 2020. "Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective," Papers 2002.00949, arXiv.org.
    5. Labidi, Manel & Gajewski, Jean François, 2019. "Does increased disclosure of intangible assets enhance liquidity around new equity offerings?," Research in International Business and Finance, Elsevier, vol. 48(C), pages 426-437.
    6. Nikolopoulos, Konstantinos I. & Babai, M. Zied & Bozos, Konstantinos, 2016. "Forecasting supply chain sporadic demand with nearest neighbor approaches," International Journal of Production Economics, Elsevier, vol. 177(C), pages 139-148.

  26. Bozos, Konstantinos & Nikolopoulos, Konstantinos & Ramgandhi, Ghanamaruthy, 2011. "Dividend signaling under economic adversity: Evidence from the London Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 364-374.

    Cited by:

    1. Fairchild, Richard & Guney, Yilmaz & Thanatawee, Yordying, 2014. "Corporate dividend policy in Thailand: Theory and evidence," International Review of Financial Analysis, Elsevier, vol. 31(C), pages 129-151.
    2. Dimitrios Koutmos & Konstantinos Bozos & Dionysia Dionysiou & Neophytos Lambertides, 2018. "The timing of new corporate debt issues and the risk-return tradeoff," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 943-978, May.
    3. Akbar, Saeed & Rehman, Shafiq ur & Ormrod, Phillip, 2013. "The impact of recent financial shocks on the financing and investment policies of UK private firms," International Review of Financial Analysis, Elsevier, vol. 26(C), pages 59-70.
    4. Robert Joliet & Aline Muller, 2015. "Dividends and Foreign Performance Signaling," Post-Print hal-01667402, HAL.
    5. Chada, Swechha & Saravanan, Palanisamy & Varadharajan, Gopal, 2024. "Socioemotional wealth and cash flow sensitivity of cash: Evidence from India," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    6. Benavides, Julian & Berggrun, Luis & Perafan, Hector, 2016. "Dividend payout policies: Evidence from Latin America," Finance Research Letters, Elsevier, vol. 17(C), pages 197-210.
    7. Renata Legenzova & Otilija Jurakovaite & Agne Galinskaite, 2017. "The Analysis of Dividend Announcement Impact on Stock Prices of Baltic Companies," Central European Business Review, Prague University of Economics and Business, vol. 2017(1), pages 61-76.
    8. Khelifa Mazouz & Yuliang Wu & Rabab Ebrahim & Abhijit Sharma, 2023. "Dividend policy, systematic liquidity risk, and the cost of equity capital," Review of Quantitative Finance and Accounting, Springer, vol. 60(3), pages 839-876, April.
    9. Ed-Dafali, Slimane & Patel, Ritesh & Iqbal, Najaf, 2023. "A bibliometric review of dividend policy literature," Research in International Business and Finance, Elsevier, vol. 65(C).
    10. Karlo Kauko, 2016. "Does Opaqueness Make Equity Capital Expensive for Banks?," Revista de Economía del Rosario, Universidad del Rosario, vol. 17(2), pages 203-227, February.
    11. Jitka Hilliard & John S. Jahera & Haoran Zhang, 2019. "The US financial crisis and corporate dividend reactions: for better or for worse?," Review of Quantitative Finance and Accounting, Springer, vol. 53(4), pages 1165-1193, November.
    12. Goyal, Abhinav & Muckley, Cal, 2013. "Cash dividends and investor protection in Asia," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 31-43.
    13. Booth, Laurence & Zhou, Jun, 2017. "Dividend policy: A selective review of results from around the world," Global Finance Journal, Elsevier, vol. 34(C), pages 1-15.

  27. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E., 2010. "Judging the judges through accuracy-implication metrics: The case of inventory forecasting," International Journal of Forecasting, Elsevier, vol. 26(1), pages 134-143, January.

    Cited by:

    1. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    2. De Baets, Shari & Harvey, Nigel, 2018. "Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support," International Journal of Forecasting, Elsevier, vol. 34(2), pages 163-180.
    3. Kourentzes, Nikolaos & Trapero, Juan R. & Barrow, Devon K., 2020. "Optimising forecasting models for inventory planning," International Journal of Production Economics, Elsevier, vol. 225(C).
    4. Chiang, Chung-Yean & Lin, Winston T. & Suresh, Nallan C., 2016. "An empirically-simulated investigation of the impact of demand forecasting on the bullwhip effect: Evidence from U.S. auto industry," International Journal of Production Economics, Elsevier, vol. 177(C), pages 53-65.
    5. Zhu, S. & van Jaarsveld, W.L. & Dekker, R., 2019. "Spare Parts Inventory Control based on Maintenance Planning," Econometric Institute Research Papers EI2019-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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    7. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
    8. Van der Auweraer, Sarah & Zhu, Sha & Boute, Robert N., 2021. "The value of installed base information for spare part inventory control," International Journal of Production Economics, Elsevier, vol. 239(C).
    9. Bruzda, Joanna, 2019. "Quantile smoothing in supply chain and logistics forecasting," International Journal of Production Economics, Elsevier, vol. 208(C), pages 122-139.
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    11. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    12. Evangelos Spiliotis & Spyros Makridakis & Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos, 2022. "Comparison of statistical and machine learning methods for daily SKU demand forecasting," Operational Research, Springer, vol. 22(3), pages 3037-3061, July.
    13. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "Predicting/hypothesizing the findings of the M5 competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1337-1345.
    14. Phillips, Christina Jane & Nikolopoulos, Konstantinos, 2019. "Forecast quality improvement with Action Research: A success story at PharmaCo," International Journal of Forecasting, Elsevier, vol. 35(1), pages 129-143.
    15. Rekik, Yacine & Syntetos, Aris & Jemai, Zied, 2015. "An e-retailing supply chain subject to inventory inaccuracies," International Journal of Production Economics, Elsevier, vol. 167(C), pages 139-155.
    16. Syntetos, Aris A. & Kholidasari, Inna & Naim, Mohamed M., 2016. "The effects of integrating management judgement into OUT levels: In or out of context?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 853-863.
    17. Ali, Mohammad M. & Boylan, John E. & Syntetos, Aris A., 2012. "Forecast errors and inventory performance under forecast information sharing," International Journal of Forecasting, Elsevier, vol. 28(4), pages 830-841.
    18. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    19. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
    20. Petropoulos, Fotios & Hyndman, Rob J. & Bergmeir, Christoph, 2018. "Exploring the sources of uncertainty: Why does bagging for time series forecasting work?," European Journal of Operational Research, Elsevier, vol. 268(2), pages 545-554.
    21. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    22. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael, 2014. "Collaborative forecasting in the food supply chain: A conceptual framework," International Journal of Production Economics, Elsevier, vol. 158(C), pages 120-135.
    23. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    24. Yavuz Acar, 2014. "Forecasting Method Selection Based on Operational Performance," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 28(1), pages 95-114.
    25. A A Syntetos & N C Georgantzas & J E Boylan & B C Dangerfield, 2011. "Judgement and supply chain dynamics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1138-1158, June.
    26. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    27. Hasni, M. & Babai, M.Z. & Aguir, M.S. & Jemai, Z., 2019. "An investigation on bootstrapping forecasting methods for intermittent demands," International Journal of Production Economics, Elsevier, vol. 209(C), pages 20-29.
    28. Wan, Xiang & Sanders, Nadia R., 2017. "The negative impact of product variety: Forecast bias, inventory levels, and the role of vertical integration," International Journal of Production Economics, Elsevier, vol. 186(C), pages 123-131.
    29. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    30. Gardner, Everette Shaw & Acar, Yavuz, 2016. "The forecastability quotient reconsidered," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1208-1211.
    31. K Nikolopoulos & A A Syntetos & J E Boylan & F Petropoulos & V Assimakopoulos, 2011. "An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 544-554, March.
    32. Mirko Kremer & Enno Siemsen & Douglas J. Thomas, 2016. "The Sum and Its Parts: Judgmental Hierarchical Forecasting," Management Science, INFORMS, vol. 62(9), pages 2745-2764, September.
    33. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
    34. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
    35. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
    36. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Quantile forecast optimal combination to enhance safety stock estimation," International Journal of Forecasting, Elsevier, vol. 35(1), pages 239-250.
    37. Petropoulos, Fotios & Fildes, Robert & Goodwin, Paul, 2016. "Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 842-852.
    38. Kolassa, Stephan, 2022. "Commentary on the M5 forecasting competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1562-1568.
    39. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    40. Saedi, Samira & Kundakcioglu, O. Erhun & Henry, Andrea C., 2016. "Mitigating the impact of drug shortages for a healthcare facility: An inventory management approach," European Journal of Operational Research, Elsevier, vol. 251(1), pages 107-123.
    41. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "The M5 competition: Background, organization, and implementation," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1325-1336.
    42. Van der Auweraer, Sarah & Boute, Robert, 2019. "Forecasting spare part demand using service maintenance information," International Journal of Production Economics, Elsevier, vol. 213(C), pages 138-149.
    43. Maud van den Broeke & Shari de Baets & Ann Vereecke & Philippe Baecke & Karlien Vanderheyden, 2019. "Judgmental forecast adjustments over different time horizons," Post-Print hal-03001747, HAL.
    44. Jussim, Maxim, 2014. "Entwicklung eines Simulationstools zur Analyse von Prognose- und Dispositionsentscheidungen im Krankenhausbereich," Bayreuth Reports on Information Systems Management 57, University of Bayreuth, Chair of Information Systems Management.
    45. Theodorou, Evangelos & Wang, Shengjie & Kang, Yanfei & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2022. "Exploring the representativeness of the M5 competition data," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1500-1506.
    46. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    47. Halkos, George & Kevork, Ilias, 2013. "Forecasting the optimal order quantity in the newsvendor model under a correlated demand," MPRA Paper 44189, University Library of Munich, Germany.
    48. Babai, M.Z. & Ali, M.M. & Boylan, J.E. & Syntetos, A.A., 2013. "Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis," International Journal of Production Economics, Elsevier, vol. 143(2), pages 463-471.
    49. Sarlo, Rodrigo & Fernandes, Cristiano & Borenstein, Denis, 2023. "Lumpy and intermittent retail demand forecasts with score-driven models," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1146-1160.
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    51. Halkos, George & Kevork, Ilias, 2012. "Unbiased estimation of maximum expected profits in the Newsvendor Model: a case study analysis," MPRA Paper 40724, University Library of Munich, Germany.
    52. Acar, Yavuz & Gardner, Everette S., 2012. "Forecasting method selection in a global supply chain," International Journal of Forecasting, Elsevier, vol. 28(4), pages 842-848.

  28. Vicky Bamiatzi & Konstantinos Bozos & Konstantinos Nikolopoulos, 2010. "On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 279-282, February.

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    1. Varum, Celeste Amorim & Rocha, Vera Catarina Barros, 2011. "Do foreign and domestic firms behave any different during economic slowdowns?," International Business Review, Elsevier, vol. 20(1), pages 48-59, February.
    2. Buchnea, Emily & Elsahn, Ziad, 2022. "Historical social network analysis: Advancing new directions for international business research," International Business Review, Elsevier, vol. 31(5).

  29. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.

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    1. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
    2. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
    3. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    4. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.

  30. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.

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    1. Cedric A. Lehmann & Christiane B. Haubitz & Andreas Fügener & Ulrich W. Thonemann, 2022. "The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3419-3434, September.
    2. De Baets, Shari & Harvey, Nigel, 2018. "Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support," International Journal of Forecasting, Elsevier, vol. 34(2), pages 163-180.
    3. Surti, Chirag & Celani, Anthony & Gajpal, Yuvraj, 2020. "The newsvendor problem: The role of prospect theory and feedback," European Journal of Operational Research, Elsevier, vol. 287(1), pages 251-261.
    4. Bolger, Fergus & Wright, George, 2017. "Use of expert knowledge to anticipate the future: Issues, analysis and directions," International Journal of Forecasting, Elsevier, vol. 33(1), pages 230-243.
    5. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
    6. Philip Hans Franses, 2021. "Modeling Judgment in Macroeconomic Forecasts," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 401-417, December.
    7. Valeria Croce & Karl W. Wöber, 2011. "Judgemental Forecasting Support Systems in Tourism," Tourism Economics, , vol. 17(4), pages 709-724, August.
    8. Anyu Liu & Laura Vici & Vicente Ramos & Sauveur Giannoni & Adam Blake, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team," Post-Print hal-04653783, HAL.
    9. Danese, Pamela & Kalchschmidt, Matteo, 2011. "The role of the forecasting process in improving forecast accuracy and operational performance," International Journal of Production Economics, Elsevier, vol. 131(1), pages 204-214, May.
    10. Spiliotis, Evangelos & Petropoulos, Fotios, 2024. "On the update frequency of univariate forecasting models," European Journal of Operational Research, Elsevier, vol. 314(1), pages 111-121.
    11. Baecke, Philippe & De Baets, Shari & Vanderheyden, Karlien, 2017. "Investigating the added value of integrating human judgement into statistical demand forecasting systems," International Journal of Production Economics, Elsevier, vol. 191(C), pages 85-96.
    12. Lin, Vera Shanshan & Goodwin, Paul & Song, Haiyan, 2014. "Accuracy and bias of experts’ adjusted forecasts," Annals of Tourism Research, Elsevier, vol. 48(C), pages 156-174.
    13. Enrique Holgado de Frutos & Juan R Trapero & Francisco Ramos, 2020. "A literature review on operational decisions applied to collaborative supply chains," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-28, March.
    14. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
    15. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E., 2010. "Judging the judges through accuracy-implication metrics: The case of inventory forecasting," International Journal of Forecasting, Elsevier, vol. 26(1), pages 134-143, January.
    16. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    17. Reimers, Stian & Harvey, Nigel, 2024. "Bars, lines and points: The effect of graph format on judgmental forecasting," International Journal of Forecasting, Elsevier, vol. 40(1), pages 44-61.
    18. Theocharis, Zoe & Harvey, Nigel, 2016. "Order effects in judgmental forecasting," International Journal of Forecasting, Elsevier, vol. 32(1), pages 44-60.
    19. Michael Pedersen, 2024. "Judgment in macroeconomic output growth predictions: Efficiency, accuracy and persistence," Papers 2404.04105, arXiv.org.
    20. Önkal, Dilek & Lawrence, Michael & Zeynep SayIm, K., 2011. "Influence of differentiated roles on group forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 27(1), pages 50-68, January.
    21. Vera Shanshan Lin, 2019. "Judgmental adjustments in tourism forecasting practice: How good are they?," Tourism Economics, , vol. 25(3), pages 402-424, May.
    22. Lorko, Matej & Servátka, Maroš & Zhang, Le, 2020. "Improving the accuracy of project schedules," MPRA Paper 103367, University Library of Munich, Germany.
    23. Trapero, Juan R. & Pedregal, Diego J. & Fildes, R. & Kourentzes, N., 2013. "Analysis of judgmental adjustments in the presence of promotions," International Journal of Forecasting, Elsevier, vol. 29(2), pages 234-243.
    24. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
    25. Mathieu Chevrier & Brice Corgnet & Eric Guerci & Julie Rosaz, 2024. "Algorithm Credulity: Human and Algorithmic Advice in Prediction Experiments," GREDEG Working Papers 2024-03, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    26. Andrey Davydenko & Paul Goodwin, 2021. "Bewertung der Verzerrung von Punktprognosen über mehrere Zeitreihen hinweg: Maßnahmen und visuelle Werkzeuge [Assessing point forecast bias across multiple time series: Measures and visual tools]," Post-Print hal-03359179, HAL.
    27. Rianne Legerstee & Philip Hans Franses, 2011. "Do Experts' SKU Forecasts improve after Feedback?," Tinbergen Institute Discussion Papers 11-135/4, Tinbergen Institute.
    28. Madhukar Nagare & Pankaj Dutta & Naoufel Cheikhrouhou, 2016. "Optimal ordering policy for newsvendor models with bidirectional changes in demand using expert judgment," OPSEARCH, Springer;Operational Research Society of India, vol. 53(3), pages 620-647, September.
    29. Fildes, Robert & Goodwin, Paul, 2021. "Stability in the inefficient use of forecasting systems: A case study in a supply chain company," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1031-1046.
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    31. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E. & Fildes, Robert & Goodwin, Paul, 2009. "The effects of integrating management judgement into intermittent demand forecasts," International Journal of Production Economics, Elsevier, vol. 118(1), pages 72-81, March.
    32. Phillips, Christina Jane & Nikolopoulos, Konstantinos, 2019. "Forecast quality improvement with Action Research: A success story at PharmaCo," International Journal of Forecasting, Elsevier, vol. 35(1), pages 129-143.
    33. Goodwin, Paul, 2015. "Is a more liberal approach to conservatism needed in forecasting?," Journal of Business Research, Elsevier, vol. 68(8), pages 1753-1754.
    34. Spithourakis, Georgios P. & Petropoulos, Fotios & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2015. "Amplifying the learning effects via a Forecasting and Foresight Support System," International Journal of Forecasting, Elsevier, vol. 31(1), pages 20-32.
    35. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    36. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    37. Syntetos, Aris A. & Kholidasari, Inna & Naim, Mohamed M., 2016. "The effects of integrating management judgement into OUT levels: In or out of context?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 853-863.
    38. Rianne Legerstee & Philip Hans Franses & Richard Paap, 2011. "Do Experts incorporate Statistical Model Forecasts and should they?," Tinbergen Institute Discussion Papers 11-141/4, Tinbergen Institute.
    39. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2022. "Classification-based model selection in retail demand forecasting," International Journal of Forecasting, Elsevier, vol. 38(1), pages 209-223.
    40. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2019. "Forecasting retailer product sales in the presence of structural change," European Journal of Operational Research, Elsevier, vol. 279(2), pages 459-470.
    41. Valeria Belvedere & Elisa Martina Martinelli & Annalisa Tunisini, 2021. "Getting the most from E-commerce in the context of omnichannel strategies," Italian Journal of Marketing, Springer, vol. 2021(4), pages 331-349, December.
    42. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.
    43. Franses, Philip Hans & Kranendonk, Henk C. & Lanser, Debby, 2011. "One model and various experts: Evaluating Dutch macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 482-495.
    44. Franses, Philip Hans & Legerstee, Rianne, 2013. "Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 80-87.
    45. Danese, Pamela & Kalchschmidt, Matteo, 2011. "The impact of forecasting on companies' performance: Analysis in a multivariate setting," International Journal of Production Economics, Elsevier, vol. 133(1), pages 458-469, September.
    46. Katarzyna Maciejowska & Jakub Nowotarski, 2015. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," HSC Research Reports HSC/15/06, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    47. Khosrowabadi, Naghmeh & Hoberg, Kai & Imdahl, Christina, 2022. "Evaluating human behaviour in response to AI recommendations for judgemental forecasting," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1151-1167.
    48. Chang, Chia Lin & Franses, Philip Hans & Mcaleer, Michael, 2012. "Evaluating Individual and Mean Non-Replicable Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 22-43, September.
    49. Hewage, Harsha Chamara & Perera, H. Niles & De Baets, Shari, 2022. "Forecast adjustments during post-promotional periods," European Journal of Operational Research, Elsevier, vol. 300(2), pages 461-472.
    50. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Empirical safety stock estimation based on kernel and GARCH models," Omega, Elsevier, vol. 84(C), pages 199-211.
    51. Chia-Lin Chang & Philip Hans Franses & Michael McAleer, 2009. "How Accurate are Government Forecasts of Economic Fundamentals? The Case of Taiwan," CIRJE F-Series CIRJE-F-637, CIRJE, Faculty of Economics, University of Tokyo.
    52. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2014. "Evaluating Macroeconomic Forecasts: A Concise Review Of Some Recent Developments," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 195-208, April.
    53. Philip Hans Franses, 2018. "Prediction Intervals For Expert-Adjusted Forecasts," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 308-320, December.
    54. Niematallah Elamin & Mototsugu Fukushige, 2017. "Integrating judgment in statistical demand forecasting: An approach to confront uncertainty," Discussion Papers in Economics and Business 17-20, Osaka University, Graduate School of Economics.
    55. Wellens, Arnoud P. & Boute, Robert N. & Udenio, Maximiliano, 2024. "Simplifying tree-based methods for retail sales forecasting with explanatory variables," European Journal of Operational Research, Elsevier, vol. 314(2), pages 523-539.
    56. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
    57. Philip Hans Franses & Rianne Legerstee & Richard Paap, 2011. "Estimating Loss Functions of Experts," Tinbergen Institute Discussion Papers 11-177/4, Tinbergen Institute.
    58. Franses, Ph.H.B.F., 2009. "Forecasting Sales," Econometric Institute Research Papers EI 2009-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    59. Spyros Makridakis & Fotios Petropoulos & Yanfei Kang, 2023. "Large Language Models: Their Success and Impact," Forecasting, MDPI, vol. 5(3), pages 1-14, August.
    60. Asimakopoulos, Stavros & Dix, Alan, 2013. "Forecasting support systems technologies-in-practice: A model of adoption and use for product forecasting," International Journal of Forecasting, Elsevier, vol. 29(2), pages 322-336.
    61. Goodwin, Paul & Sinan Gönül, M. & Önkal, Dilek, 2013. "Antecedents and effects of trust in forecasting advice," International Journal of Forecasting, Elsevier, vol. 29(2), pages 354-366.
    62. De Baets, Shari & Harvey, Nigel, 2020. "Using judgment to select and adjust forecasts from statistical models," European Journal of Operational Research, Elsevier, vol. 284(3), pages 882-895.
    63. Markus Jung & Mischa Seiter, 2021. "Towards a better understanding on mitigating algorithm aversion in forecasting: an experimental study," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 32(4), pages 495-516, December.
    64. Petropoulos, Fotios & Goodwin, Paul & Fildes, Robert, 2017. "Using a rolling training approach to improve judgmental extrapolations elicited from forecasters with technical knowledge," International Journal of Forecasting, Elsevier, vol. 33(1), pages 314-324.
    65. Hasin Md. Muhtasim Taqi & Humaira Nafisa Ahmed & Sumit Paul & Maryam Garshasbi & Syed Mithun Ali & Golam Kabir & Sanjoy Kumar Paul, 2020. "Strategies to Manage the Impacts of the COVID-19 Pandemic in the Supply Chain: Implications for Improving Economic and Social Sustainability," Sustainability, MDPI, vol. 12(22), pages 1-25, November.
    66. Belvedere, Valeria & Goodwin, Paul, 2017. "The influence of product involvement and emotion on short-term product demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 652-661.
    67. R Fildes & B Kingsman, 2011. "Incorporating demand uncertainty and forecast error in supply chain planning models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 483-500, March.
    68. Liu, Anyu & Vici, Laura & Ramos, Vicente & Giannoni, Sauveur & Blake, Adam, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team," Annals of Tourism Research, Elsevier, vol. 88(C).
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    77. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    78. Franses, Philip Hans, 2013. "Improving judgmental adjustment of model-based forecasts," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 1-8.
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    80. Thomson, Mary E. & Pollock, Andrew C. & Gönül, M. Sinan & Önkal, Dilek, 2013. "Effects of trend strength and direction on performance and consistency in judgmental exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 29(2), pages 337-353.
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    82. Fildes, Robert & Goodwin, Paul & Onkal, Dilek, 2015. "Information use in supply chain forecasting," MPRA Paper 66034, University Library of Munich, Germany.
    83. Wan, Xiang & Sanders, Nadia R., 2017. "The negative impact of product variety: Forecast bias, inventory levels, and the role of vertical integration," International Journal of Production Economics, Elsevier, vol. 186(C), pages 123-131.
    84. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    85. Sroginis, Anna & Fildes, Robert & Kourentzes, Nikolaos, 2023. "Use of contextual and model-based information in adjusting promotional forecasts," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1177-1191.
    86. Yun Shin Lee & Enno Siemsen, 2017. "Task Decomposition and Newsvendor Decision Making," Management Science, INFORMS, vol. 63(10), pages 3226-3245, October.
    87. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
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    92. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    93. Phillip M. Yelland & Shinji Kim & Renée Stratulate, 2010. "A Bayesian Model for Sales Forecasting at Sun Microsystems," Interfaces, INFORMS, vol. 40(2), pages 118-129, April.
    94. Markus A. Fitza, 2017. "How much do CEOs really matter? Reaffirming that the CEO effect is mostly due to chance," Strategic Management Journal, Wiley Blackwell, vol. 38(3), pages 802-811, March.
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    96. Mirko Kremer & Enno Siemsen & Douglas J. Thomas, 2016. "The Sum and Its Parts: Judgmental Hierarchical Forecasting," Management Science, INFORMS, vol. 62(9), pages 2745-2764, September.
    97. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    98. Goodwin, Paul & Gönül, M. Sinan & Önkal, Dilek, 2019. "When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions," European Journal of Operational Research, Elsevier, vol. 273(3), pages 992-1004.
    99. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
    100. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2021. "Distributional regression for demand forecasting in e-grocery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 831-842.
    101. Trapero, Juan R. & Kourentzes, N. & Fildes, R., 2012. "Impact of information exchange on supplier forecasting performance," Omega, Elsevier, vol. 40(6), pages 738-747.
    102. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    103. Andrey Davydenko & Paul Goodwin, 2021. "Assessing Point Forecast Bias Across Multiple Time Series: Measures and Visual Tools," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(5), pages 1-46, September.
    104. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Quantile forecast optimal combination to enhance safety stock estimation," International Journal of Forecasting, Elsevier, vol. 35(1), pages 239-250.
    105. Fildes, Robert & Goodwin, Paul & Önkal, Dilek, 2019. "Use and misuse of information in supply chain forecasting of promotion effects," International Journal of Forecasting, Elsevier, vol. 35(1), pages 144-156.
    106. Önkal, Dilek & Lawrence, Michael & Zeynep Sayım, K., 2011. "Influence of differentiated roles on group forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 27(1), pages 50-68.
    107. Petropoulos, Fotios & Fildes, Robert & Goodwin, Paul, 2016. "Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 842-852.
    108. Philip Hans Franses, 2011. "Averaging Model Forecasts and Expert Forecasts: Why Does It Work?," Interfaces, INFORMS, vol. 41(2), pages 177-181, April.
    109. Alvarado-Valencia, Jorge & Barrero, Lope H. & Önkal, Dilek & Dennerlein, Jack T., 2017. "Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(1), pages 298-313.
    110. Wright, George & Goodwin, Paul, 2009. "Decision making and planning under low levels of predictability: Enhancing the scenario method," International Journal of Forecasting, Elsevier, vol. 25(4), pages 813-825, October.
    111. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
    112. Mirko Kremer & Brent Moritz & Enno Siemsen, 2011. "Demand Forecasting Behavior: System Neglect and Change Detection," Management Science, INFORMS, vol. 57(10), pages 1827-1843, October.
    113. Goodwin, Paul & Önkal, Dilek & Stekler, Herman O., 2018. "What if you are not Bayesian? The consequences for decisions involving risk," European Journal of Operational Research, Elsevier, vol. 266(1), pages 238-246.
    114. Franses, Ph.H.B.F., 2010. "Decomposing bias in expert forecast," Econometric Institute Research Papers EI 2010-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    115. Sagaert, Yves R. & Aghezzaf, El-Houssaine & Kourentzes, Nikolaos & Desmet, Bram, 2018. "Tactical sales forecasting using a very large set of macroeconomic indicators," European Journal of Operational Research, Elsevier, vol. 264(2), pages 558-569.
    116. , Aisdl, 2020. "Becoming Attuned," OSF Preprints j7f8y, Center for Open Science.
    117. Önkal, Dilek & Sinan Gönül, M. & Goodwin, Paul & Thomson, Mary & Öz, Esra, 2017. "Evaluating expert advice in forecasting: Users’ reactions to presumed vs. experienced credibility," International Journal of Forecasting, Elsevier, vol. 33(1), pages 280-297.
    118. Maud van den Broeke & Shari de Baets & Ann Vereecke & Philippe Baecke & Karlien Vanderheyden, 2019. "Judgmental forecast adjustments over different time horizons," Post-Print hal-03001747, HAL.
    119. Sagaert, Yves R. & Kourentzes, Nikolaos & De Vuyst, Stijn & Aghezzaf, El-Houssaine & Desmet, Bram, 2019. "Incorporating macroeconomic leading indicators in tactical capacity planning," International Journal of Production Economics, Elsevier, vol. 209(C), pages 12-19.
    120. Önkal, Dilek & Zeynep Sayım, K. & Lawrence, Michael, 2012. "Wisdom of group forecasts: Does role-playing play a role?," Omega, Elsevier, vol. 40(6), pages 693-702.
    121. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    122. Goodwin, Paul, 2015. "When simple alternatives to Bayes formula work well: Reducing the cognitive load when updating probability forecasts," Journal of Business Research, Elsevier, vol. 68(8), pages 1686-1691.
    123. Boutselis, Petros & McNaught, Ken, 2019. "Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context," International Journal of Production Economics, Elsevier, vol. 209(C), pages 325-333.
    124. Bert de Bruijn & Philip Hans Franses, 2012. "Managing Sales Forecasters," Tinbergen Institute Discussion Papers 12-131/III, Tinbergen Institute.
    125. Atanasov, Pavel & Witkowski, Jens & Ungar, Lyle & Mellers, Barbara & Tetlock, Philip, 2020. "Small steps to accuracy: Incremental belief updaters are better forecasters," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 19-35.
    126. Fildes, Robert, 2015. "Forecasters and rationality—A comment on Fritsche et al., Forecasting the Brazilian Real and Mexican Peso: Asymmetric loss, forecast rationality and forecaster herding," International Journal of Forecasting, Elsevier, vol. 31(1), pages 140-143.
    127. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
    128. F Caniato & M Kalchschmidt & S Ronchi, 2011. "Integrating quantitative and qualitative forecasting approaches: organizational learning in an action research case," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 413-424, March.
    129. Franses, Ph.H.B.F. & Maassen, N.R., 2015. "Consensus forecasters: How good are they individually and why?," Econometric Institute Research Papers EI2015-21, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    130. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael & Önkal, Dilek, 2019. "Judgmental adjustments through supply integration for strategic partnerships in food chains," Omega, Elsevier, vol. 87(C), pages 20-33.
    131. Christiane B. Haubitz & Cedric A. Lehmann & Andreas Fügener & Ulrich W. Thonemann, 2021. "The Risk of Algorithm Transparency: How Algorithm Complexity Drives the Effects on Use of Advice," ECONtribute Discussion Papers Series 078, University of Bonn and University of Cologne, Germany.
    132. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    133. Chang, C-L. & Franses, Ph.H.B.F. & McAleer, M.J., 2009. "How Accurate are Government Forecast of Economic Fundamentals?," Econometric Institute Research Papers EI 2009-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    134. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    135. Victory Ikpe & Mohammad Shamsuddoha, 2024. "Functional Model of Supply Chain Waste Reduction and Control Strategies for Retailers—The USA Retail Industry," Logistics, MDPI, vol. 8(1), pages 1-17, February.
    136. Song, Haiyan & Gao, Bastian Z. & Lin, Vera S., 2013. "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system," International Journal of Forecasting, Elsevier, vol. 29(2), pages 295-310.

  31. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E. & Fildes, Robert & Goodwin, Paul, 2009. "The effects of integrating management judgement into intermittent demand forecasts," International Journal of Production Economics, Elsevier, vol. 118(1), pages 72-81, March.

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    2. Surti, Chirag & Celani, Anthony & Gajpal, Yuvraj, 2020. "The newsvendor problem: The role of prospect theory and feedback," European Journal of Operational Research, Elsevier, vol. 287(1), pages 251-261.
    3. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
    4. Baecke, Philippe & De Baets, Shari & Vanderheyden, Karlien, 2017. "Investigating the added value of integrating human judgement into statistical demand forecasting systems," International Journal of Production Economics, Elsevier, vol. 191(C), pages 85-96.
    5. Syntetos, A.A. & Babai, M.Z. & Davies, J. & Stephenson, D., 2010. "Forecasting and stock control: A study in a wholesaling context," International Journal of Production Economics, Elsevier, vol. 127(1), pages 103-111, September.
    6. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
    7. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E., 2010. "Judging the judges through accuracy-implication metrics: The case of inventory forecasting," International Journal of Forecasting, Elsevier, vol. 26(1), pages 134-143, January.
    8. Vera Shanshan Lin, 2019. "Judgmental adjustments in tourism forecasting practice: How good are they?," Tourism Economics, , vol. 25(3), pages 402-424, May.
    9. Trapero, Juan R. & Pedregal, Diego J. & Fildes, R. & Kourentzes, N., 2013. "Analysis of judgmental adjustments in the presence of promotions," International Journal of Forecasting, Elsevier, vol. 29(2), pages 234-243.
    10. Petropoulos, Fotios & Wang, Xun & Disney, Stephen M., 2019. "The inventory performance of forecasting methods: Evidence from the M3 competition data," International Journal of Forecasting, Elsevier, vol. 35(1), pages 251-265.
    11. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
    12. Rekik, Yacine & Syntetos, Aris & Jemai, Zied, 2015. "An e-retailing supply chain subject to inventory inaccuracies," International Journal of Production Economics, Elsevier, vol. 167(C), pages 139-155.
    13. Syntetos, Aris A. & Kholidasari, Inna & Naim, Mohamed M., 2016. "The effects of integrating management judgement into OUT levels: In or out of context?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 853-863.
    14. Franses, Philip Hans & Legerstee, Rianne, 2013. "Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 80-87.
    15. Teunter, Ruud H. & Syntetos, Aris A. & Zied Babai, M., 2011. "Intermittent demand: Linking forecasting to inventory obsolescence," European Journal of Operational Research, Elsevier, vol. 214(3), pages 606-615, November.
    16. Pennings, Clint L.P. & van Dalen, Jan & van der Laan, Erwin A., 2017. "Exploiting elapsed time for managing intermittent demand for spare parts," European Journal of Operational Research, Elsevier, vol. 258(3), pages 958-969.
    17. Franses, Ph.H.B.F., 2009. "Forecasting Sales," Econometric Institute Research Papers EI 2009-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
    19. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    20. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael, 2014. "Collaborative forecasting in the food supply chain: A conceptual framework," International Journal of Production Economics, Elsevier, vol. 158(C), pages 120-135.
    21. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
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    23. A A Syntetos & N C Georgantzas & J E Boylan & B C Dangerfield, 2011. "Judgement and supply chain dynamics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1138-1158, June.
    24. Haines, Russell & Hough, Jill & Haines, Douglas, 2017. "A metacognitive perspective on decision making in supply chains: Revisiting the behavioral causes of the bullwhip effect," International Journal of Production Economics, Elsevier, vol. 184(C), pages 7-20.
    25. Franses, Philip Hans, 2013. "Improving judgmental adjustment of model-based forecasts," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 1-8.
    26. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    27. Wan, Xiang & Sanders, Nadia R., 2017. "The negative impact of product variety: Forecast bias, inventory levels, and the role of vertical integration," International Journal of Production Economics, Elsevier, vol. 186(C), pages 123-131.
    28. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    29. Sroginis, Anna & Fildes, Robert & Kourentzes, Nikolaos, 2023. "Use of contextual and model-based information in adjusting promotional forecasts," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1177-1191.
    30. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    31. Romeijnders, Ward & Teunter, Ruud & van Jaarsveld, Willem, 2012. "A two-step method for forecasting spare parts demand using information on component repairs," European Journal of Operational Research, Elsevier, vol. 220(2), pages 386-393.
    32. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    33. Van der Auweraer, Sarah & Boute, Robert N. & Syntetos, Aris A., 2019. "Forecasting spare part demand with installed base information: A review," International Journal of Forecasting, Elsevier, vol. 35(1), pages 181-196.
    34. Tian, Xin & Wang, Haoqing & E, Erjiang, 2021. "Forecasting intermittent demand for inventory management by retailers: A new approach," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    35. de Bruijn, L.P. & Franses, Ph.H.B.F., 2011. "Evaluating the Rationality of Managers' Sales Forecasts," Econometric Institute Research Papers EI 2011-36, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    36. Ferbar Tratar, Liljana, 2015. "Forecasting method for noisy demand," International Journal of Production Economics, Elsevier, vol. 161(C), pages 64-73.
    37. Boulaksil, Y. & Franses, Ph.H.B.F., 2008. "Experts' Stated Behavior," ERIM Report Series Research in Management ERS-2008-001-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    38. Mendes, Paulo & Leal, José Eugênio & Thomé, Antônio Márcio Tavares, 2016. "A maturity model for demand-driven supply chains in the consumer product goods industry," International Journal of Production Economics, Elsevier, vol. 179(C), pages 153-165.
    39. P H Franses & R Legerstee, 2011. "Experts' adjustment to model-based SKU-level forecasts: does the forecast horizon matter?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 537-543, March.
    40. Philip Hans Franses & Rianne Legerstee, 2010. "Do experts' adjustments on model-based SKU-level forecasts improve forecast quality?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 331-340.
    41. Van der Auweraer, Sarah & Boute, Robert, 2019. "Forecasting spare part demand using service maintenance information," International Journal of Production Economics, Elsevier, vol. 213(C), pages 138-149.
    42. Maud van den Broeke & Shari de Baets & Ann Vereecke & Philippe Baecke & Karlien Vanderheyden, 2019. "Judgmental forecast adjustments over different time horizons," Post-Print hal-03001747, HAL.
    43. Babai, M.Z. & Ali, M.M. & Boylan, J.E. & Syntetos, A.A., 2013. "Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis," International Journal of Production Economics, Elsevier, vol. 143(2), pages 463-471.
    44. Altay, Nezih & Litteral, Lewis A. & Rudisill, Frank, 2012. "Effects of correlation on intermittent demand forecasting and stock control," International Journal of Production Economics, Elsevier, vol. 135(1), pages 275-283.
    45. Önkal, Dilek & Zeynep Sayım, K. & Lawrence, Michael, 2012. "Wisdom of group forecasts: Does role-playing play a role?," Omega, Elsevier, vol. 40(6), pages 693-702.
    46. Bacchetti, A. & Plebani, F. & Saccani, N. & Syntetos, A.A., 2013. "Empirically-driven hierarchical classification of stock keeping units," International Journal of Production Economics, Elsevier, vol. 143(2), pages 263-274.
    47. Bert de Bruijn & Philip Hans Franses, 2012. "Managing Sales Forecasters," Tinbergen Institute Discussion Papers 12-131/III, Tinbergen Institute.
    48. F Caniato & M Kalchschmidt & S Ronchi, 2011. "Integrating quantitative and qualitative forecasting approaches: organizational learning in an action research case," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 413-424, March.
    49. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael & Önkal, Dilek, 2019. "Judgmental adjustments through supply integration for strategic partnerships in food chains," Omega, Elsevier, vol. 87(C), pages 20-33.
    50. Song, Haiyan & Gao, Bastian Z. & Lin, Vera S., 2013. "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system," International Journal of Forecasting, Elsevier, vol. 29(2), pages 295-310.

  32. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.

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    3. Qian, Lixian & Soopramanien, Didier, 2014. "Using diffusion models to forecast market size in emerging markets with applications to the Chinese car market," Journal of Business Research, Elsevier, vol. 67(6), pages 1226-1232.
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    5. Syntetos, A.A. & Babai, M.Z. & Davies, J. & Stephenson, D., 2010. "Forecasting and stock control: A study in a wholesaling context," International Journal of Production Economics, Elsevier, vol. 127(1), pages 103-111, September.
    6. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
    7. M M Ali & J E Boylan, 2011. "Feasibility principles for Downstream Demand Inference in supply chains," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 474-482, March.
    8. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
    9. Zenker, Frank & Witte, Erich H., 2021. "Three aspects of an empirical effect: statistical, theoretical, and practical aspect," OSF Preprints zng8k, Center for Open Science.
    10. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    11. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    12. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
    13. Lee, Chaehwa & Wilhelm, Wilbert, 2010. "On integrating theories of international economics in the strategic planning of global supply chains and facility location," International Journal of Production Economics, Elsevier, vol. 124(1), pages 225-240, March.
    14. Chun, Young H., 2012. "Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing," European Journal of Operational Research, Elsevier, vol. 217(3), pages 673-678.
    15. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2019. "Forecasting retailer product sales in the presence of structural change," European Journal of Operational Research, Elsevier, vol. 279(2), pages 459-470.
    16. Bahman Rostami-Tabar & Mohammad M Ali & Tao Hong & Rob J Hyndman & Michael D Porter & Aris Syntetos, 2020. "Forecasting for Social Good," Monash Econometrics and Business Statistics Working Papers 37/20, Monash University, Department of Econometrics and Business Statistics.
    17. Teunter, Ruud H. & Syntetos, Aris A. & Zied Babai, M., 2011. "Intermittent demand: Linking forecasting to inventory obsolescence," European Journal of Operational Research, Elsevier, vol. 214(3), pages 606-615, November.
    18. Babai, M.Z. & Boylan, J.E. & Syntetos, A.A. & Ali, M.M., 2016. "Reduction of the value of information sharing as demand becomes strongly auto-correlated," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 130-135.
    19. Hewage, Harsha Chamara & Perera, H. Niles & De Baets, Shari, 2022. "Forecast adjustments during post-promotional periods," European Journal of Operational Research, Elsevier, vol. 300(2), pages 461-472.
    20. Li, Qinyun & Disney, Stephen M. & Gaalman, Gerard, 2014. "Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy," International Journal of Production Economics, Elsevier, vol. 149(C), pages 3-16.
    21. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    22. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    23. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    24. Ramanathan, Usha & Muyldermans, Luc, 2010. "Identifying demand factors for promotional planning and forecasting: A case of a soft drink company in the UK," International Journal of Production Economics, Elsevier, vol. 128(2), pages 538-545, December.
    25. Prestwich, S.D. & Tarim, S.A. & Rossi, R. & Hnich, B., 2014. "Forecasting intermittent demand by hyperbolic-exponential smoothing," International Journal of Forecasting, Elsevier, vol. 30(4), pages 928-933.
    26. Ralph D. Snyder & Anne B. Koehler, 2008. "A View of Damped Trend as Incorporating a Tracking Signal into a State Space Model," Monash Econometrics and Business Statistics Working Papers 7/08, Monash University, Department of Econometrics and Business Statistics.
    27. Bin Shen & Hau-Ling Chan, 2017. "Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-26, February.
    28. Paris A. Mastorocostas & Constantinos S. Hilas & Dimitris N. Varsamis & Stergiani C. Dova, 2016. "Telecommunications call volume forecasting with a block-diagonal recurrent fuzzy neural network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 63(1), pages 15-25, September.
    29. Tsionas, Mike G., 2021. "Bayesian forecasting with the structural damped trend model," International Journal of Production Economics, Elsevier, vol. 234(C).
    30. van Donselaar, K.H. & Peters, J. & de Jong, A. & Broekmeulen, R.A.C.M., 2016. "Analysis and forecasting of demand during promotions for perishable items," International Journal of Production Economics, Elsevier, vol. 172(C), pages 65-75.
    31. McKenzie, Eddie & Gardner Jr., Everette S., 2010. "Damped trend exponential smoothing: A modelling viewpoint," International Journal of Forecasting, Elsevier, vol. 26(4), pages 661-665, October.
    32. Zvi Schwartz & Timothy Webb & Jean-Pierre I van der Rest & Larissa Koupriouchina, 2021. "Enhancing the accuracy of revenue management system forecasts: The impact of machine and human learning on the effectiveness of hotel occupancy forecast combinations across multiple forecasting horizo," Tourism Economics, , vol. 27(2), pages 273-291, March.
    33. J W Taylor, 2011. "Multi-item sales forecasting with total and split exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 555-563, March.
    34. R J Ormerod, 2010. "OR as rational choice: a decision and game theory perspective," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(12), pages 1761-1776, December.
    35. Syntetos, Aris A. & Boylan, John E., 2010. "On the variance of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 128(2), pages 546-555, December.
    36. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    37. Gardner, Everette S., 2015. "Conservative forecasting with the damped trend," Journal of Business Research, Elsevier, vol. 68(8), pages 1739-1741.
    38. Mehmet Güray Güler, 2019. "Advertising and forecasting investments of a newsvendor," 4OR, Springer, vol. 17(1), pages 45-73, March.
    39. Snyder, Ralph D. & Koehler, Anne B., 2009. "Incorporating a tracking signal into a state space model," International Journal of Forecasting, Elsevier, vol. 25(3), pages 526-530, July.
    40. Babai, M. Zied & Ali, Mohammad M. & Nikolopoulos, Konstantinos, 2012. "Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis," Omega, Elsevier, vol. 40(6), pages 713-721.
    41. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    42. Yves R. Sagaert & El-Houssaine Aghezzaf & Nikolaos Kourentzes & Bram Desmet, 2018. "Temporal Big Data for Tactical Sales Forecasting in the Tire Industry," Interfaces, INFORMS, vol. 48(2), pages 121-129, April.
    43. Prestwich, S.D. & Tarim, S.A. & Rossi, R., 2021. "Intermittency and obsolescence: A Croston method with linear decay," International Journal of Forecasting, Elsevier, vol. 37(2), pages 708-715.
    44. Villegas, Marco A. & Pedregal, Diego J., 2019. "Automatic selection of unobserved components models for supply chain forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 157-169.
    45. M Günther & C Stummer & L M Wakolbinger & M Wildpaner, 2011. "An agent-based simulation approach for the new product diffusion of a novel biomass fuel," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 12-20, January.
    46. Sachs, Anna-Lena & Minner, Stefan, 2014. "The data-driven newsvendor with censored demand observations," International Journal of Production Economics, Elsevier, vol. 149(C), pages 28-36.
    47. Wang, Wenbin & Syntetos, Aris A., 2011. "Spare parts demand: Linking forecasting to equipment maintenance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1194-1209.
    48. Alvarado-Valencia, Jorge & Barrero, Lope H. & Önkal, Dilek & Dennerlein, Jack T., 2017. "Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(1), pages 298-313.
    49. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
    50. P H Franses & R Legerstee, 2011. "Experts' adjustment to model-based SKU-level forecasts: does the forecast horizon matter?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 537-543, March.
    51. Sule Birim & Ipek Kazancoglu & Sachin Kumar Mangla & Aysun Kahraman & Yigit Kazancoglu, 2024. "The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods," Annals of Operations Research, Springer, vol. 339(1), pages 131-161, August.
    52. B Baesens & C Mues & D Martens & J Vanthienen, 2009. "50 years of data mining and OR: upcoming trends and challenges," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 16-23, May.
    53. S Tsafarakis & E Grigoroudis & N Matsatsinis, 2011. "Consumer choice behaviour and new product development: an integrated market simulation approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(7), pages 1253-1267, July.
    54. Stefanny Ramirez & Laurence H. Brandenburg & Dario Bauso, 2023. "Coordinated Replenishment Game and Learning Under Time Dependency and Uncertainty of the Parameters," Dynamic Games and Applications, Springer, vol. 13(1), pages 326-352, March.
    55. Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.
    56. Maud van den Broeke & Shari de Baets & Ann Vereecke & Philippe Baecke & Karlien Vanderheyden, 2019. "Judgmental forecast adjustments over different time horizons," Post-Print hal-03001747, HAL.
    57. Jussim, Maxim, 2014. "Entwicklung eines Simulationstools zur Analyse von Prognose- und Dispositionsentscheidungen im Krankenhausbereich," Bayreuth Reports on Information Systems Management 57, University of Bayreuth, Chair of Information Systems Management.
    58. J. D’Haen & D. Van Den Poel & D. Thorleuchter, 2012. "Predicting Customer Profitability During Acquisition: Finding the Optimal Combination of Data Source and Data Mining Technique," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/818, Ghent University, Faculty of Economics and Business Administration.
    59. Pennings, Clint L.P. & van Dalen, Jan, 2017. "Integrated hierarchical forecasting," European Journal of Operational Research, Elsevier, vol. 263(2), pages 412-418.
    60. Giacomo Sbrana, 2010. "Forecasting damped trend exponential smoothing: an algebraic viewpoint," Working Papers 10-08, Association Française de Cliométrie (AFC).
    61. Zied Babai, Mohamed & Syntetos, Aris & Teunter, Ruud, 2014. "Intermittent demand forecasting: An empirical study on accuracy and the risk of obsolescence," International Journal of Production Economics, Elsevier, vol. 157(C), pages 212-219.
    62. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    63. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.
    64. E. Vercher & A. Corberán-Vallet & J. Segura & J. Bermúdez, 2012. "Initial conditions estimation for improving forecast accuracy in exponential smoothing," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 517-533, July.
    65. Devon Barrow & Antonija Mitrovic & Jay Holland & Mohammad Ali & Nikolaos Kourentzes, 2024. "Developing Personalised Learning Support for the Business Forecasting Curriculum: The Forecasting Intelligent Tutoring System," Forecasting, MDPI, vol. 6(1), pages 1-20, March.
    66. F Caniato & M Kalchschmidt & S Ronchi, 2011. "Integrating quantitative and qualitative forecasting approaches: organizational learning in an action research case," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 413-424, March.
    67. Banerjee, Nilabhra & Morton, Alec & Akartunalı, Kerem, 2020. "Passenger demand forecasting in scheduled transportation," European Journal of Operational Research, Elsevier, vol. 286(3), pages 797-810.
    68. B D Williams & M A Waller, 2011. "Estimating a retailer's base stock level: an optimal distribution center order forecast policy," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 662-666, April.
    69. A A Syntetos & M Z Babai & Y Dallery & R Teunter, 2009. "Periodic control of intermittent demand items: theory and empirical analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 611-618, May.

  33. Lee, Wing Yee & Goodwin, Paul & Fildes, Robert & Nikolopoulos, Konstantinos & Lawrence, Michael, 2007. "Providing support for the use of analogies in demand forecasting tasks," International Journal of Forecasting, Elsevier, vol. 23(3), pages 377-390.

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    1. Alexander Estes & David J. Lovell & Michael O. Ball, 2019. "Unsupervised prototype reduction for data exploration and an application to air traffic management initiatives," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 467-510, December.
    2. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
    3. Lu, Emiao & Handl, Julia & Xu, Dong-ling, 2018. "Determining analogies based on the integration of multiple information sources," International Journal of Forecasting, Elsevier, vol. 34(3), pages 507-528.
    4. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    5. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    6. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
    7. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    8. Hewage, Harsha Chamara & Perera, H. Niles & De Baets, Shari, 2022. "Forecast adjustments during post-promotional periods," European Journal of Operational Research, Elsevier, vol. 300(2), pages 461-472.
    9. van Donselaar, K.H. & Peters, J. & de Jong, A. & Broekmeulen, R.A.C.M., 2016. "Analysis and forecasting of demand during promotions for perishable items," International Journal of Production Economics, Elsevier, vol. 172(C), pages 65-75.
    10. Konstantinos Nikolopoulos & Waleed S. Alghassab & Konstantia Litsiou & Stelios Sapountzis, 2019. "Long-Term Economic Forecasting with Structured Analogies and Interaction Groups," Working Papers 19018, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    11. Fildes, Robert & Goodwin, Paul & Onkal, Dilek, 2015. "Information use in supply chain forecasting," MPRA Paper 66034, University Library of Munich, Germany.
    12. Dohnal, Mirko, 2016. "Complex biofuels related scenarios generated by qualitative reasoning under severe information shortages: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 676-684.
    13. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    14. Sroginis, Anna & Fildes, Robert & Kourentzes, Nikolaos, 2023. "Use of contextual and model-based information in adjusting promotional forecasts," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1177-1191.
    15. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    16. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    17. Goodwin, Paul & Fildes, Robert & Lawrence, Michael & Stephens, Greg, 2011. "Restrictiveness and guidance in support systems," Omega, Elsevier, vol. 39(3), pages 242-253, June.
    18. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    19. Phillip M. Yelland & Shinji Kim & Renée Stratulate, 2010. "A Bayesian Model for Sales Forecasting at Sun Microsystems," Interfaces, INFORMS, vol. 40(2), pages 118-129, April.
    20. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    21. Lee, Hakyeon & Kim, Sang Gook & Park, Hyun-woo & Kang, Pilsung, 2014. "Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 49-64.
    22. Fildes, Robert & Goodwin, Paul & Önkal, Dilek, 2019. "Use and misuse of information in supply chain forecasting of promotion effects," International Journal of Forecasting, Elsevier, vol. 35(1), pages 144-156.
    23. Cote, Joseph A., 2011. "Predicting elections from biographical information about candidates: A commentary essay," Journal of Business Research, Elsevier, vol. 64(7), pages 696-698, July.
    24. Alvarado-Valencia, Jorge & Barrero, Lope H. & Önkal, Dilek & Dennerlein, Jack T., 2017. "Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(1), pages 298-313.
    25. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.
    26. Nikolopoulos, Konstantinos & Litsa, Akrivi & Petropoulos, Fotios & Bougioukos, Vasileios & Khammash, Marwan, 2015. "Relative performance of methods for forecasting special events," Journal of Business Research, Elsevier, vol. 68(8), pages 1785-1791.
    27. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    28. F Caniato & M Kalchschmidt & S Ronchi, 2011. "Integrating quantitative and qualitative forecasting approaches: organizational learning in an action research case," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 413-424, March.
    29. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    30. Solvoll, Gisle & Mathisen, Terje Andreas & Welde, Morten, 2020. "Forecasting air traffic demand for major infrastructure changes," Research in Transportation Economics, Elsevier, vol. 82(C).

  34. K. Maris & K. Nikolopoulos & K. Giannelos & V. Assimakopoulos, 2007. "Options trading driven by volatility directional accuracy," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 253-260.

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    1. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    2. Chuang Yuang Lin & Dar Hsin Chen & Chin Yu Tsai, 2011. "The limitation of monotonicity property of option prices: an empirical evidence," Applied Economics, Taylor & Francis Journals, vol. 43(23), pages 3103-3113.
    3. Alex YiHou Huang, 2012. "Volatility forecasting by quantile regression," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 423-433, February.
    4. Vicky Bamiatzi & Konstantinos Bozos & Konstantinos Nikolopoulos, 2010. "On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 279-282, February.

  35. Goodwin, Paul & Fildes, Robert & Lawrence, Michael & Nikolopoulos, Konstantinos, 2007. "The process of using a forecasting support system," International Journal of Forecasting, Elsevier, vol. 23(3), pages 391-404.

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    1. De Baets, Shari & Harvey, Nigel, 2018. "Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support," International Journal of Forecasting, Elsevier, vol. 34(2), pages 163-180.
    2. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
    3. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E. & Fildes, Robert & Goodwin, Paul, 2009. "The effects of integrating management judgement into intermittent demand forecasts," International Journal of Production Economics, Elsevier, vol. 118(1), pages 72-81, March.
    4. Spithourakis, Georgios P. & Petropoulos, Fotios & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2015. "Amplifying the learning effects via a Forecasting and Foresight Support System," International Journal of Forecasting, Elsevier, vol. 31(1), pages 20-32.
    5. Daniel Antony Kolkman & Paolo Campo & Tina Balke-Visser & Nigel Gilbert, 2016. "How to build models for government: criteria driving model acceptance in policymaking," Policy Sciences, Springer;Society of Policy Sciences, vol. 49(4), pages 489-504, December.
    6. Smith, Carlo D. & Mentzer, John T., 2010. "Forecasting task-technology fit: The influence of individuals, systems and procedures on forecast performance," International Journal of Forecasting, Elsevier, vol. 26(1), pages 144-161, January.
    7. Asimakopoulos, Stavros & Dix, Alan, 2013. "Forecasting support systems technologies-in-practice: A model of adoption and use for product forecasting," International Journal of Forecasting, Elsevier, vol. 29(2), pages 322-336.
    8. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    9. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    10. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    11. Petropoulos, Fotios & Fildes, Robert & Goodwin, Paul, 2016. "Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 842-852.
    12. F Caniato & M Kalchschmidt & S Ronchi, 2011. "Integrating quantitative and qualitative forecasting approaches: organizational learning in an action research case," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 413-424, March.
    13. Song, Haiyan & Gao, Bastian Z. & Lin, Vera S., 2013. "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system," International Journal of Forecasting, Elsevier, vol. 29(2), pages 295-310.

  36. Bagos Pantelis G & Nikolopoulos Georgios K, 2007. "A Method for Meta-Analysis of Case-Control Genetic Association Studies Using Logistic Regression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-28, June.

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  37. Nikolopoulos, K. & Goodwin, P. & Patelis, A. & Assimakopoulos, V., 2007. "Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches," European Journal of Operational Research, Elsevier, vol. 180(1), pages 354-368, July.

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    1. Giwoong Bae & Hye-jin Kim, 2022. "The impact of online video highlights on TV audience ratings," Electronic Commerce Research, Springer, vol. 22(2), pages 405-425, June.
    2. Lessmann, Stefan & Voß, Stefan, 2017. "Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy," International Journal of Forecasting, Elsevier, vol. 33(4), pages 864-877.
    3. Wauters, Mathieu & Vanhoucke, Mario, 2017. "A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1097-1111.
    4. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    5. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    6. Servranckx, Tom & Vanhoucke, Mario & Aouam, Tarik, 2021. "Practical application of reference class forecasting for cost and time estimations: Identifying the properties of similarity," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1161-1179.
    7. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    8. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    9. Ciyun Lin & Kang Wang & Dayong Wu & Bowen Gong, 2020. "Passenger Flow Prediction Based on Land Use around Metro Stations: A Case Study," Sustainability, MDPI, vol. 12(17), pages 1-22, August.
    10. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
    11. Katsikopoulos, Konstantinos V. & Durbach, Ian N. & Stewart, Theodor J., 2018. "When should we use simple decision models? A synthesis of various research strands," Omega, Elsevier, vol. 81(C), pages 17-25.
    12. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael, 2014. "Collaborative forecasting in the food supply chain: A conceptual framework," International Journal of Production Economics, Elsevier, vol. 158(C), pages 120-135.
    13. Bozos, Konstantinos & Nikolopoulos, Konstantinos, 2011. "Forecasting the value effect of seasoned equity offering announcements," European Journal of Operational Research, Elsevier, vol. 214(2), pages 418-427, October.
    14. Dinesh Reddy Vangumalli & Konstantinos Nikolopoulos & Konstantia Litsiou, 2019. "Clustering, Forecasting and Cluster Forecasting: using k-medoids, k-NNs and random forests for cluster selection," Working Papers 19016, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    15. S. Buxton & Kostas Nikolopoulos & M. Khammash & P. Stern, 2015. "Modelling and Forecasting Branded and Generic Pharmaceutical Life Cycles: Assessment of the Number of Dispensed Units," Working Papers 15004, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    16. Lee, Wing Yee & Goodwin, Paul & Fildes, Robert & Nikolopoulos, Konstantinos & Lawrence, Michael, 2007. "Providing support for the use of analogies in demand forecasting tasks," International Journal of Forecasting, Elsevier, vol. 23(3), pages 377-390.
    17. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    18. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    19. Jiang, Wuhao & Wang, Kai & Lv, Yan & Guo, Jianfeng & Ni, Zhongjin & Ni, Yihua, 2020. "Time series based behavior pattern quantification analysis and prediction — A study on animal behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    20. Danaher, Peter J. & Dagger, Tracey S. & Smith, Michael S., 2011. "Forecasting television ratings," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1215-1240, October.
    21. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    22. Tine Van Calster & Filip Van den Bossche & Bart Baesens & Wilfried Lemahieu, 2020. "Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective," Papers 2002.00949, arXiv.org.
    23. Vincenzo Candila & Lucio Palazzo, 2020. "Neural Networks and Betting Strategies for Tennis," Risks, MDPI, vol. 8(3), pages 1-19, June.
    24. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    25. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.
    26. Nikolopoulos, Konstantinos & Litsa, Akrivi & Petropoulos, Fotios & Bougioukos, Vasileios & Khammash, Marwan, 2015. "Relative performance of methods for forecasting special events," Journal of Business Research, Elsevier, vol. 68(8), pages 1785-1791.
    27. Danaher, Peter & Dagger, Tracey, 2012. "Using a nested logit model to forecast television ratings," International Journal of Forecasting, Elsevier, vol. 28(3), pages 607-622.
    28. Van Reeth, Daam, 2019. "Forecasting Tour de France TV audiences: A multi-country analysis," International Journal of Forecasting, Elsevier, vol. 35(2), pages 810-821.
    29. Nikolopoulos, Konstantinos I. & Babai, M. Zied & Bozos, Konstantinos, 2016. "Forecasting supply chain sporadic demand with nearest neighbor approaches," International Journal of Production Economics, Elsevier, vol. 177(C), pages 139-148.

  38. Fildes, Robert & Nikolopoulos, Konstantinos, 2006. "Spyros Makridakis: An interview with the International Journal of Forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 625-636.

    Cited by:

    1. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    2. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    3. Armstrong, J. Scott & Fildes, Robert, 2006. "Making progress in forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 433-441.
    4. Dejian Yu & Libo Sheng & Shunshun Shi, 2023. "A retrospective analysis of Journal of Forecasting: From 1982 to 2019," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 1008-1035, July.
    5. Goldstein, Daniel G. & Gigerenzer, Gerd, 2009. "Fast and frugal forecasting," International Journal of Forecasting, Elsevier, vol. 25(4), pages 760-772, October.
    6. Fildes, Robert, 2006. "The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion," International Journal of Forecasting, Elsevier, vol. 22(3), pages 415-432.
    7. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.

  39. C. Petropoulos & K. Nikolopoulos & A. Patelis & V. Assimakopoulos & D. Askounis, 2006. "Tourism Technical Analysis System," Tourism Economics, , vol. 12(4), pages 543-563, December.

    Cited by:

    1. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.

  40. C. Petropoulos & K. Nikolopoulos & A. Patelis & V. Assimakopoulos, 2005. "A technical analysis approach to tourism demand forecasting," Applied Economics Letters, Taylor & Francis Journals, vol. 12(6), pages 327-333.

    Cited by:

    1. Day, Min-Yuh & Ni, Yensen & Huang, Paoyu, 2019. "Trading as sharp movements in oil prices and technical trading signals emitted with big data concerns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 349-372.
    2. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    3. Eleonora Di Matteo & Paolo Roma & Santo Zafonte & Umberto Panniello & Lorenzo Abbate, 2021. "Development of a Decision Support System Framework for Cultural Heritage Management," Sustainability, MDPI, vol. 13(13), pages 1-27, June.
    4. C. Petropoulos & K. Nikolopoulos & A. Patelis & V. Assimakopoulos & D. Askounis, 2006. "Tourism Technical Analysis System," Tourism Economics, , vol. 12(4), pages 543-563, December.
    5. Dr. Murat çuhadar & Iclal Cogurcu & Ceyda Kukrer, 2014. "Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures," International Journal of Business and Social Research, LAR Center Press, vol. 4(3), pages 12-28, March.
    6. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886, July.
    7. Lin, Tun & De Guzman, Franklin, 2007. "Tourism for pro-poor and sustainable growth: economic analysis of tourism projects," MPRA Paper 24994, University Library of Munich, Germany.
    8. Vicky Bamiatzi & Konstantinos Bozos & Konstantinos Nikolopoulos, 2010. "On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 279-282, February.
    9. Marcos Álvarez-Díaz & Manuel González-Gómez & María Soledad Otero-Giráldez, 2018. "Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming," Forecasting, MDPI, vol. 1(1), pages 1-17, September.
    10. Dr. Murat çuhadar & Iclal Cogurcu & Ceyda Kukrer, 2014. "Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(3), pages 12-28, March.

  41. K. Maris & G. Pantou & K. Nikolopoulos & E. PagourtzI & V. Assimakopoulos, 2004. "A study of financial volatility forecasting techniques in the FTSE/ASE 20 index," Applied Economics Letters, Taylor & Francis Journals, vol. 11(7), pages 453-457.

    Cited by:

    1. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    2. Dimitrios Thomakos & Michail Koubouros, 2008. "The Role of Realized Volatility in the Athens Stock Exchange," Working Papers 0020, University of Peloponnese, Department of Economics.
    3. K. Maris & K. Nikolopoulos & K. Giannelos & V. Assimakopoulos, 2007. "Options trading driven by volatility directional accuracy," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 253-260.
    4. Vicky Bamiatzi & Konstantinos Bozos & Konstantinos Nikolopoulos, 2010. "On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 279-282, February.

  42. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.

    Cited by:

    1. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    2. Van Belle, Jente & Crevits, Ruben & Verbeke, Wouter, 2023. "Improving forecast stability using deep learning," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1333-1350.
    3. Dimitrios D. Thomakos & Konstantinos Nikolopoulos, 2013. "Forecasting multivariate time series with the Theta Method," Working Papers 13004, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    4. Rob J Hyndman, 2019. "A Brief History of Forecasting Competitions," Monash Econometrics and Business Statistics Working Papers 3/19, Monash University, Department of Econometrics and Business Statistics.
    5. Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    6. Jaganathan, Srihari & Prakash, P.K.S., 2020. "A combination-based forecasting method for the M4-competition," International Journal of Forecasting, Elsevier, vol. 36(1), pages 98-104.
    7. Lu, Emiao & Handl, Julia & Xu, Dong-ling, 2018. "Determining analogies based on the integration of multiple information sources," International Journal of Forecasting, Elsevier, vol. 34(3), pages 507-528.
    8. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    9. Bruzda, Joanna, 2019. "Quantile smoothing in supply chain and logistics forecasting," International Journal of Production Economics, Elsevier, vol. 208(C), pages 122-139.
    10. Petropoulos, Fotios & Wang, Xun & Disney, Stephen M., 2019. "The inventory performance of forecasting methods: Evidence from the M3 competition data," International Journal of Forecasting, Elsevier, vol. 35(1), pages 251-265.
    11. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    12. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    13. Aleksandr N. Grekov & Elena V. Vyshkvarkova & Aleksandr S. Mavrin, 2024. "Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms," Forecasting, MDPI, vol. 6(2), pages 1-14, May.
    14. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "Predicting/hypothesizing the findings of the M5 competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1337-1345.
    15. Spithourakis, Georgios P. & Petropoulos, Fotios & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2015. "Amplifying the learning effects via a Forecasting and Foresight Support System," International Journal of Forecasting, Elsevier, vol. 31(1), pages 20-32.
    16. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    17. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    18. Spiliotis, Evangelos & Kouloumos, Andreas & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Are forecasting competitions data representative of the reality?," International Journal of Forecasting, Elsevier, vol. 36(1), pages 37-53.
    19. Miller, Don M. & Williams, Dan, 2003. "Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 19(4), pages 669-684.
    20. Bahodirhon Safarov & Hisham Mohammad Al-Smadi & Makhina Buzrukova & Bekzot Janzakov & Alexandru Ilieş & Vasile Grama & Dorina Camelia Ilieș & Katalin Csobán Vargáné & Lóránt Dénes Dávid, 2022. "Forecasting the Volume of Tourism Services in Uzbekistan," Sustainability, MDPI, vol. 14(13), pages 1-18, June.
    21. K. Maris & K. Nikolopoulos & K. Giannelos & V. Assimakopoulos, 2007. "Options trading driven by volatility directional accuracy," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 253-260.
    22. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
    23. Hyndman, R.J. & Billah, B., 2001. "Unmasking the Theta Method," Monash Econometrics and Business Statistics Working Papers 5/01, Monash University, Department of Econometrics and Business Statistics.
    24. Fotios Petropoulos & Enno Siemsen, 2023. "Forecast Selection and Representativeness," Management Science, INFORMS, vol. 69(5), pages 2672-2690, May.
    25. Andrea Kolková & Aleksandr Kljuènikov, 2021. "Demand forecasting: an alternative approach based on technical indicator Pbands," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 1063-1094, December.
    26. Fiorucci, Jose Augusto & Louzada, Francisco, 2020. "GROEC: Combination method via Generalized Rolling Origin Evaluation," International Journal of Forecasting, Elsevier, vol. 36(1), pages 105-109.
    27. Kang, Yanfei & Cao, Wei & Petropoulos, Fotios & Li, Feng, 2022. "Forecast with forecasts: Diversity matters," European Journal of Operational Research, Elsevier, vol. 301(1), pages 180-190.
    28. Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics.
    29. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
    30. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    31. Wellens, Arnoud P. & Boute, Robert N. & Udenio, Maximiliano, 2024. "Simplifying tree-based methods for retail sales forecasting with explanatory variables," European Journal of Operational Research, Elsevier, vol. 314(2), pages 523-539.
    32. Jo~ao B. Assunc{c}~ao & Pedro Afonso Fernandes, 2022. "Nowcasting the Portuguese GDP with Monthly Data," Papers 2206.06823, arXiv.org.
    33. Nikolopoulos, Konstantinos & Buxton, Samantha & Khammash, Marwan & Stern, Philip, 2016. "Forecasting branded and generic pharmaceuticals," International Journal of Forecasting, Elsevier, vol. 32(2), pages 344-357.
    34. Erjiang E & Ming Yu & Xin Tian & Ye Tao, 2022. "Dynamic Model Selection Based on Demand Pattern Classification in Retail Sales Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-16, September.
    35. Matheus Henrique Dal Molin Ribeiro & Stéfano Frizzo Stefenon & José Donizetti de Lima & Ademir Nied & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning," Energies, MDPI, vol. 13(19), pages 1-22, October.
    36. Ji Wu & Xian Cheng & Stephen Shaoyi Liao, 2020. "Tourism forecast combination using the stochastic frontier analysis technique," Tourism Economics, , vol. 26(7), pages 1086-1107, November.
    37. João A. Bastos, 2019. "Forecasting the capacity of mobile networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(2), pages 231-242, October.
    38. Sanchez-Ubeda, Eugenio Fco. & Berzosa, Ana, 2007. "Modeling and forecasting industrial end-use natural gas consumption," Energy Economics, Elsevier, vol. 29(4), pages 710-742, July.
    39. Kourentzes, Nikolaos & Saayman, Andrea & Jean-Pierre, Philippe & Provenzano, Davide & Sahli, Mondher & Seetaram, Neelu & Volo, Serena, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team," Annals of Tourism Research, Elsevier, vol. 88(C).
    40. Gardner Jr., Everette S. & Diaz-Saiz, Joaquin, 2008. "Exponential smoothing in the telecommunications data," International Journal of Forecasting, Elsevier, vol. 24(1), pages 170-174.
    41. Aviral Kumar Tiwari & Claudiu T Albulescu & Phouphet Kyophilavong, 2014. "A comparison of different forecasting models of the international trade in India," Economics Bulletin, AccessEcon, vol. 34(1), pages 420-429.
    42. Sbrana, Giacomo & Silvestrini, Andrea, 2023. "The RWDAR model: A novel state-space approach to forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 922-937.
    43. George Athanasopoulos & Rob J Hyndman & Haiyan Song & Doris C Wu, 2008. "The tourism forecasting competition," Monash Econometrics and Business Statistics Working Papers 10/08, Monash University, Department of Econometrics and Business Statistics, revised Oct 2009.
    44. Gardner, Everette S., 2015. "Conservative forecasting with the damped trend," Journal of Business Research, Elsevier, vol. 68(8), pages 1739-1741.
    45. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2019. "Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors," International Journal of Production Economics, Elsevier, vol. 209(C), pages 92-102.
    46. S. Buxton & Kostas Nikolopoulos & M. Khammash & P. Stern, 2015. "Modelling and Forecasting Branded and Generic Pharmaceutical Life Cycles: Assessment of the Number of Dispensed Units," Working Papers 15004, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    47. Athanasopoulos, George & Hyndman, Rob J., 2011. "The value of feedback in forecasting competitions," International Journal of Forecasting, Elsevier, vol. 27(3), pages 845-849, July.
    48. Dean W. Wichern & Benito E. Flores, 2005. "Evaluating forecasts: a look at aggregate bias and accuracy measures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 433-451.
    49. Hess, Alexander & Spinler, Stefan & Winkenbach, Matthias, 2021. "Real-time demand forecasting for an urban delivery platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    50. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    51. Wellens, Arnoud P. & Udenio, Maxi & Boute, Robert N., 2022. "Transfer learning for hierarchical forecasting: Reducing computational efforts of M5 winning methods," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1482-1491.
    52. C. Petropoulos & K. Nikolopoulos & A. Patelis & V. Assimakopoulos & D. Askounis, 2006. "Tourism Technical Analysis System," Tourism Economics, , vol. 12(4), pages 543-563, December.
    53. Rob J Hyndman & Maxwell L. King & Ivet Pitrun & Baki Billah, 2002. "Local Linear Forecasts Using Cubic Smoothing Splines," Monash Econometrics and Business Statistics Working Papers 10/02, Monash University, Department of Econometrics and Business Statistics.
    54. Rajapaksha, Dilini & Bergmeir, Christoph & Hyndman, Rob J., 2023. "LoMEF: A framework to produce local explanations for global model time series forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1424-1447.
    55. Petropoulos, Fotios & Svetunkov, Ivan, 2020. "A simple combination of univariate models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 110-115.
    56. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    57. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    58. Dimitrios Sarris & Evangelos Spiliotis & Vassilios Assimakopoulos, 2020. "Exploiting resampling techniques for model selection in forecasting: an empirical evaluation using out-of-sample tests," Operational Research, Springer, vol. 20(2), pages 701-721, June.
    59. K Nikolopoulos & A A Syntetos & J E Boylan & F Petropoulos & V Assimakopoulos, 2011. "An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 544-554, March.
    60. Karamaziotis, Panagiotis I. & Raptis, Achilleas & Nikolopoulos, Konstantinos & Litsiou, Konstantia & Assimakopoulos, Vassilis, 2020. "An empirical investigation of water consumption forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(2), pages 588-606.
    61. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
    62. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    63. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
    64. Andrea Kolková & Petr Rozehnal, 2022. "Hybrid demand forecasting models: pre-pandemic and pandemic use studies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(3), pages 699-725, September.
    65. Fildes, Robert & Petropoulos, Fotios, 2015. "Is there a Golden Rule?," Journal of Business Research, Elsevier, vol. 68(8), pages 1742-1745.
    66. João B. Assunção & Pedro Afonso Fernandes, 2022. "Nowcasting GDP: An Application to Portugal," Forecasting, MDPI, vol. 4(3), pages 1-15, August.
    67. Magdalena Sycińska-Dziarnowska & Liliana Szyszka-Sommerfeld & Karolina Kłoda & Michele Simeone & Krzysztof Woźniak & Gianrico Spagnuolo, 2021. "Mental Health Interest and Its Prediction during the COVID-19 Pandemic Using Google Trends," IJERPH, MDPI, vol. 18(23), pages 1-14, November.
    68. Philip Hans Franses, 2020. "IMA(1,1) as a new benchmark for forecast evaluation," Applied Economics Letters, Taylor & Francis Journals, vol. 27(17), pages 1419-1423, October.
    69. Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten, 2023. "Parameter-efficient deep probabilistic forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 332-345.
    70. Rostami-Tabar, Bahman & Disney, Stephen M., 2023. "On the order-up-to policy with intermittent integer demand and logically consistent forecasts," International Journal of Production Economics, Elsevier, vol. 257(C).
    71. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Generalizing the Theta method for automatic forecasting," European Journal of Operational Research, Elsevier, vol. 284(2), pages 550-558.
    72. Hollyman, Ross & Petropoulos, Fotios & Tipping, Michael E., 2021. "Understanding forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 294(1), pages 149-160.
    73. Kang, Yanfei & Hyndman, Rob J. & Smith-Miles, Kate, 2017. "Visualising forecasting algorithm performance using time series instance spaces," International Journal of Forecasting, Elsevier, vol. 33(2), pages 345-358.
    74. G P Girish & Aviral Kumar Tiwari, 2016. "A comparison of different univariate forecasting models forSpot Electricity Price in India," Economics Bulletin, AccessEcon, vol. 36(2), pages 1039-1057.
    75. Sofia-ira KTENA & Fotios PETROPOULOS & Polychronis KOUTSOLIAKOS & Dimitrios MICHOS & Vassilios ASSIMAKOPOULOS, 2011. "Forecasting Sales in a Sugar Factory," Journal of Knowledge Management, Economics and Information Technology, ScientificPapers.org, vol. 1(7), pages 1-12, December.
    76. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    77. Godahewa, Rakshitha & Bergmeir, Christoph & Webb, Geoffrey I. & Montero-Manso, Pablo, 2023. "An accurate and fully-automated ensemble model for weekly time series forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 641-658.
    78. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    79. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    80. Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos, 2023. "Shrinkage estimator for exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1351-1365.
    81. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    82. Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701.
    83. Fiorucci, Jose A. & Pellegrini, Tiago R. & Louzada, Francisco & Petropoulos, Fotios & Koehler, Anne B., 2016. "Models for optimising the theta method and their relationship to state space models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1151-1161.
    84. Vicky Bamiatzi & Konstantinos Bozos & Konstantinos Nikolopoulos, 2010. "On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 279-282, February.
    85. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    86. Fildes, Robert & Petropoulos, Fotios, 2013. "An evaluation of simple forecasting model selection rules," MPRA Paper 51772, University Library of Munich, Germany.
    87. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    88. Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.
    89. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    90. Nikolopoulos, Konstantinos & Petropoulos, Fotios & Rodrigues, Vasco Sanchez & Pettit, Stephen & Beresford, Anthony, 2022. "A disaster response model driven by spatial–temporal forecasts," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1214-1220.
    91. Ma, Shaohui & Fildes, Robert, 2020. "Forecasting third-party mobile payments with implications for customer flow prediction," International Journal of Forecasting, Elsevier, vol. 36(3), pages 739-760.
    92. Meenakshi Narayan & Ann Majewicz Fey, 2020. "Developing a novel force forecasting technique for early prediction of critical events in robotics," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-34, May.
    93. Panja, Madhurima & Chakraborty, Tanujit & Nadim, Sk Shahid & Ghosh, Indrajit & Kumar, Uttam & Liu, Nan, 2023. "An ensemble neural network approach to forecast Dengue outbreak based on climatic condition," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    94. Shaub, David, 2020. "Fast and accurate yearly time series forecasting with forecast combinations," International Journal of Forecasting, Elsevier, vol. 36(1), pages 116-120.
    95. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.

Chapters

  1. Konstantinos I. Nikolopoulos & Andreas I. Tsalas, 2017. "Non-performing Loans: A Review of the Literature and the International Experience," Palgrave Macmillan Studies in Banking and Financial Institutions, in: Platon Monokroussos & Christos Gortsos (ed.), Non-Performing Loans and Resolving Private Sector Insolvency, chapter 3, pages 47-68, Palgrave Macmillan.

    Cited by:

    1. Rahbar , Farhad & Behzadi Soufiani , Mohsen, 2021. "The Impact of Macroeconomic and Banking Variables on Non-Performing Loans in Oil Cycles: Evidence from Iran," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 16(2), pages 135-164, June.
    2. Liu, Suyi & Jin, Justin & Nainar, Khalid, 2023. "Does ESG performance reduce banks’ nonperforming loans?," Finance Research Letters, Elsevier, vol. 55(PA).
    3. Uddin, Md Hamid & Mollah, Sabur & Ali, Md Hakim, 2020. "Does cyber tech spending matter for bank stability?," International Review of Financial Analysis, Elsevier, vol. 72(C).
    4. Evzen Kocenda & Ichiro Iwasaki, 2020. "Bank Survival in Central and Eastern Europe," KIER Working Papers 1022, Kyoto University, Institute of Economic Research.
    5. Florian Manz, 2019. "Determinants of non-performing loans: What do we know? A systematic review and avenues for future research," Management Review Quarterly, Springer, vol. 69(4), pages 351-389, November.
    6. Mariagrazia Fallanca & Antonio Fabio Forgione & Edoardo Otranto, 2021. "Do the Determinants of Non-Performing Loans Have a Different Effect over Time? A Conditional Correlation Approach," JRFM, MDPI, vol. 14(1), pages 1-15, January.

Books

  1. Dimitrios D. Thomakos & Konstantinos I. Nikolopoulos (ed.), 2017. "Taxation in Crisis," Palgrave Macmillan Studies in Banking and Financial Institutions, Palgrave Macmillan, number 978-3-319-65310-5, June.

    Cited by:

    1. Karen Tumanyants, 2018. "Economic impact of the change in tax rate on small enterprises of manufacturing and construction sectors: Evidence from Russia 2006-2014," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(3), pages 642-658, June.
    2. Zacharia Zabsonre, 2023. "Steuern und Wirtschaftswachstum in der UEMOA [Taxes and economic growth in the WAEMU]," Post-Print hal-04116532, HAL.
    3. Bethencourt, Carlos & Kunze, Lars, 2020. "Social norms and economic growth in a model with labor and capital income tax evasion," Economic Modelling, Elsevier, vol. 86(C), pages 170-182.
    4. Athanasios ANASTASIOU & Vasiliki KREMASTIOTI, 2021. "The impact of taxation on growth: the case of Greece," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(627), S), pages 285-293, Summer.
    5. Janina Kotlinska & Marian Zukowski & Pawel Marzec & Jaroslaw Kuspit & Zdzislaw A. Blasiak, 2020. "Household Consumption and VAT Revenue in Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 580-605.

  2. Dimitrios D. Thomakos & Platon Monokroussos & Konstantinos I. Nikolopoulos (ed.), 2015. "A Financial Crisis Manual," Palgrave Macmillan Studies in Banking and Financial Institutions, Palgrave Macmillan, number 978-1-137-44830-9, June.

    Cited by:

    1. Michael G. Arghyrou, 2015. "The Greek Crisis and Financial Assistance Programmes: An Evaluation," CESifo Working Paper Series 5591, CESifo.

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