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‘Horses for Courses’ in demand forecasting

Citations

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  1. 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.
  2. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  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.
  9. 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.
  10. 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.
  11. 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.
  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. López Menéndez, Ana Jesús & Pérez Suárez, Rigoberto, 2017. "Forecasting Performance and Information Measures. Revisiting the M-Competition /Evaluación de Predicciones y Medidas de Información. Reexamen de la M-Competición," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 299-314, Mayo.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. Kostas Nikolopoulos & F. Petropoulos, 2015. "Forecasting, Foresight and Strategic Planning for Black Swans," Working Papers 15003, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
  23. 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.
  24. 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).
  25. 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. 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.
  27. 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.
  28. 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.
  29. You-Shyang Chen & Arun Kumar Sangaiah & Yu-Pei Lin, 2024. "Hyperautomation on fuzzy data dredging on four advanced industrial forecasting models to support sustainable business management," Annals of Operations Research, Springer, vol. 342(1), pages 215-264, November.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. Price, Ilan & Fowkes, Jaroslav & Hopman, Daniel, 2019. "Gaussian processes for unconstraining demand," European Journal of Operational Research, Elsevier, vol. 275(2), pages 621-634.
  36. 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.
  37. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
  38. Mareček, Jakub & Richtárik, Peter & Takáč, Martin, 2017. "Matrix completion under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 256(1), pages 35-43.
  39. 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.
  40. 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.
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