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Statistical and Machine Learning forecasting methods: Concerns and ways forward

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Cited by:

  1. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
  2. Amin Eshkiti & Fatemeh Sabouhi & Ali Bozorgi-Amiri, 2023. "A data-driven optimization model to response to COVID-19 pandemic: a case study," Annals of Operations Research, Springer, vol. 328(1), pages 337-386, September.
  3. Simone Sbaraglia & Alessandro Fiori Maccioni & Stefano Zedda, 2024. "Optimizing Energy Storage Profits: A New Metric for Evaluating Price Forecasting Models," JRFM, MDPI, vol. 17(12), pages 1-29, November.
  4. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
  5. Montero-Manso, Pablo & Hyndman, Rob J., 2021. "Principles and algorithms for forecasting groups of time series: Locality and globality," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1632-1653.
  6. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique," Energies, MDPI, vol. 16(18), pages 1-23, September.
  7. Rasaizadi, Arash & Farzin, Iman & Hafizi, Fateme, 2022. "Machine learning approach versus probabilistic approach to model the departure time of non-mandatory trips," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
  8. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
  9. Luca Massidda & Marino Marrocu, 2018. "Smart Meter Forecasting from One Minute to One Year Horizons," Energies, MDPI, vol. 11(12), pages 1-16, December.
  10. Ozancan Ozdemir & Ceylan Yozgatligil, 2024. "Forecasting performance of machine learning, time series, and hybrid methods for low‐ and high‐frequency time series," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(2), pages 441-474, May.
  11. 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.
  12. Terrén-Serrano, G. & Martínez-Ramón, M., 2023. "Kernel learning for intra-hour solar forecasting with infrared sky images and cloud dynamic feature extraction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
  13. Yuxi Heluo & Kexin Wang & Charles W. Robson, 2023. "Do we listen to what we are told? An empirical study on human behaviour during the COVID-19 pandemic: neural networks vs. regression analysis," Papers 2311.13046, arXiv.org.
  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. 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.
  16. 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.
  17. Pannee Suanpang & Pitchaya Jamjuntr, 2024. "Machine Learning Models for Solar Power Generation Forecasting in Microgrid Application Implications for Smart Cities," Sustainability, MDPI, vol. 16(14), pages 1-29, July.
  18. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
  19. 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.
  20. Andreea Valeria Vesa & Tudor Cioara & Ionut Anghel & Marcel Antal & Claudia Pop & Bogdan Iancu & Ioan Salomie & Vasile Teodor Dadarlat, 2020. "Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs," Sustainability, MDPI, vol. 12(4), pages 1-23, February.
  21. Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur, 2023. "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," International Journal of Forecasting, Elsevier, vol. 39(2), pages 884-900.
  22. Faisal Khalil & Gordon Pipa, 2022. "Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 147-171, June.
  23. Suder, Marcin & Gurgul, Henryk & Barbosa, Belem & Machno, Artur & Lach, Łukasz, 2024. "Effectiveness of ATM withdrawal forecasting methods under different market conditions," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
  24. 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.
  25. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
  26. Rybinski, Krzysztof, 2021. "Ranking professional forecasters by the predictive power of their narratives," International Journal of Forecasting, Elsevier, vol. 37(1), pages 186-204.
  27. Weronika Ormaniec & Marcin Pitera & Sajad Safarveisi & Thorsten Schmidt, 2022. "Estimating value at risk: LSTM vs. GARCH," Papers 2207.10539, arXiv.org.
  28. de Rezende, Rafael & Egert, Katharina & Marin, Ignacio & Thompson, Guilherme, 2022. "A white-boxed ISSM approach to estimate uncertainty distributions of Walmart sales," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1460-1467.
  29. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
  30. Yang, Hu & Chen, Yu & Chen, Kedong & Wang, Haijun, 2024. "Temporal-spatial dependencies enhanced deep learning model for time series forecast," International Review of Financial Analysis, Elsevier, vol. 94(C).
  31. Olivares, Kin G. & Meetei, O. Nganba & Ma, Ruijun & Reddy, Rohan & Cao, Mengfei & Dicker, Lee, 2024. "Probabilistic hierarchical forecasting with deep Poisson mixtures," International Journal of Forecasting, Elsevier, vol. 40(2), pages 470-489.
  32. 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.
  33. 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.
  34. Vaia I. Kontopoulou & Athanasios D. Panagopoulos & Ioannis Kakkos & George K. Matsopoulos, 2023. "A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks," Future Internet, MDPI, vol. 15(8), pages 1-31, July.
  35. Florian Rzepka & Philipp Hematty & Mano Schmitz & Julia Kowal, 2023. "Neural Network Architecture for Determining the Aging of Stationary Storage Systems in Smart Grids," Energies, MDPI, vol. 16(17), pages 1-20, August.
  36. Marya Butt & Ander de Keijzer, 2022. "Using Transfer Learning to Train a Binary Classifier for Lorrca Ektacytometery Microscopic Images of Sickle Cells and Healthy Red Blood Cells," Data, MDPI, vol. 7(9), pages 1-21, September.
  37. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • 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.
  38. 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.
  39. González Grandón, T. & Schwenzer, J. & Steens, T. & Breuing, J., 2024. "Electricity demand forecasting with hybrid classical statistical and machine learning algorithms: Case study of Ukraine," Applied Energy, Elsevier, vol. 355(C).
  40. Sun-Feel Yang & So-Won Choi & Eul-Bum Lee, 2023. "A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices," Energies, MDPI, vol. 16(11), pages 1-39, May.
  41. Alex Coad & Stjepan Srhoj, 2020. "Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms," Small Business Economics, Springer, vol. 55(3), pages 541-565, October.
  42. Witold Orzeszko, 2021. "Nonlinear Causality between Crude Oil Prices and Exchange Rates: Evidence and Forecasting," Energies, MDPI, vol. 14(19), pages 1-16, September.
  43. 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.
  44. Ortega, Luz C. & Otero, Luis Daniel & Solomon, Mitchell & Otero, Carlos E. & Fabregas, Aldo, 2023. "Deep learning models for visibility forecasting using climatological data," International Journal of Forecasting, Elsevier, vol. 39(2), pages 992-1004.
  45. J Rhet Montana & Luis A. Souto Arias & Pasquale Cirillo & Cornelis W. Oosterlee, 2024. "Quantum Majorization in Market Crash Prediction," Risks, MDPI, vol. 12(12), pages 1-18, December.
  46. Syed Ali Jafar Zaidi & Saad Tariq & Samir Brahim Belhaouari, 2021. "Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier," Data, MDPI, vol. 6(11), pages 1-18, November.
  47. Chen, Xia & Geyer, Philipp, 2022. "Machine assistance in energy-efficient building design: A predictive framework toward dynamic interaction with human decision-making under uncertainty," Applied Energy, Elsevier, vol. 307(C).
  48. 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.
  49. UÄŸur Åžener & Salvatore Joseph Terregrossa, 2024. "A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts," SAGE Open, , vol. 14(3), pages 21582440241, August.
  50. Seok-Jun Bu & Sung-Bae Cho, 2020. "Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-16, September.
  51. Satopää, Ville A. & Salikhov, Marat & Tetlock, Philip E. & Mellers, Barbara, 2023. "Decomposing the effects of crowd-wisdom aggregators: The bias–information–noise (BIN) model," International Journal of Forecasting, Elsevier, vol. 39(1), pages 470-485.
  52. Juan Tenorio & Wilder Perez, 2024. "Monthly GDP nowcasting with Machine Learning and Unstructured Data," Papers 2402.04165, arXiv.org.
  53. Ivașcu Codruț, 2023. "Can Machine Learning Models Predict Inflation?," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1748-1756, July.
  54. Wodecki Andrzej, 2020. "The Reserve Price Optimization for Publishers on Real-Time Bidding on-Line Marketplaces with Time-Series Forecasting," Foundations of Management, Sciendo, vol. 12(1), pages 167-180, January.
  55. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
  56. 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.
  57. Rotem Zelingher, 2024. "Democratising Agricultural Commodity Price Forecasting: The AGRICAF Approach," Papers 2410.20363, arXiv.org, revised Feb 2025.
  58. Apostolos Ampountolas & Mark Legg, 2024. "Predicting daily hotel occupancy: a practical application for independent hotels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(3), pages 197-205, June.
  59. Angelo Garangau Menezes & Saulo Martiello Mastelini, 2021. "MegazordNet: combining statistical and machine learning standpoints for time series forecasting," Papers 2107.01017, arXiv.org.
  60. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
  61. 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).
  62. M Belén Salas & David Alaminos & Manuel Angel Fernández & Francisco López-Valverde, 2020. "A global prediction model for sudden stops of capital flows using decision trees," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-22, February.
  63. Twumasi, Clement & Twumasi, Juliet, 2022. "Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1258-1277.
  64. 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.
  65. Theo Berger & Jana Koubová, 2024. "Forecasting Bitcoin returns: Econometric time series analysis vs. machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2904-2916, November.
  66. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
  67. Elías, Antonio & Jiménez, Raúl & Shang, Han Lin, 2022. "On projection methods for functional time series forecasting," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  68. Chong, Daniel Jia Sheng & Chan, Yi Jing & Arumugasamy, Senthil Kumar & Yazdi, Sara Kazemi & Lim, Jun Wei, 2023. "Optimisation and performance evaluation of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the prediction of biogas production ," Energy, Elsevier, vol. 266(C).
  69. Traianos-Ioannis Theodorou & Alexandros Zamichos & Michalis Skoumperdis & Anna Kougioumtzidou & Kalliopi Tsolaki & Dimitris Papadopoulos & Thanasis Patsios & George Papanikolaou & Athanasios Konstanti, 2021. "An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements," Future Internet, MDPI, vol. 13(6), pages 1-22, May.
  70. Olatunji, Kehinde O. & Ahmed, Noor A. & Madyira, Daniel M. & Adebayo, Ademola O. & Ogunkunle, Oyetola & Adeleke, Oluwatobi, 2022. "Performance evaluation of ANFIS and RSM modeling in predicting biogas and methane yields from Arachis hypogea shells pretreated with size reduction," Renewable Energy, Elsevier, vol. 189(C), pages 288-303.
  71. Roberto Casado-Vara & Angel Martin del Rey & Daniel Pérez-Palau & Luis de-la-Fuente-Valentín & Juan M. Corchado, 2021. "Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training," Mathematics, MDPI, vol. 9(4), pages 1-21, February.
  72. Anna Almosova & Niek Andresen, 2023. "Nonlinear inflation forecasting with recurrent neural networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 240-259, March.
  73. Ioannis Papageorgiou & Ioannis Kontoyiannis, 2023. "The Bayesian Context Trees State Space Model for time series modelling and forecasting," Papers 2308.00913, arXiv.org, revised Oct 2023.
  74. 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.
  75. Md Sabbirul Haque & Md Shahedul Amin & Jonayet Miah, 2023. "Retail Demand Forecasting: A Comparative Study for Multivariate Time Series," Papers 2308.11939, arXiv.org.
  76. Stef, Nicolae & Başağaoğlu, Hakan & Chakraborty, Debaditya & Ben Jabeur, Sami, 2023. "Does institutional quality affect CO2 emissions? Evidence from explainable artificial intelligence models," Energy Economics, Elsevier, vol. 124(C).
  77. Michael R. Johnson & Hiten Naik & Wei Siang Chan & Jesse Greiner & Matt Michaleski & Dong Liu & Bruno Silvestre & Ian P. McCarthy, 2023. "Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions," Health Care Management Science, Springer, vol. 26(3), pages 477-500, September.
  78. Andrés García-Medina & Ester Aguayo-Moreno, 2024. "LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1511-1542, April.
  79. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
  80. Arrieta-Prieto, Mario & Schell, Kristen R., 2022. "Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model," International Journal of Forecasting, Elsevier, vol. 38(1), pages 300-320.
  81. 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.
  82. González-Sopeña, J.M. & Pakrashi, V. & Ghosh, B., 2021. "An overview of performance evaluation metrics for short-term statistical wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  83. Fatemeh Tajik & Mingzheng Wang & Xiaohui Zhang & Jie Han, 2020. "Evaluation of the impact of body mass index on venous thromboembolism risk factors," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.
  84. Hakan Pabuccu & Serdar Ongan & Ayse Ongan, 2023. "Forecasting the movements of Bitcoin prices: an application of machine learning algorithms," Papers 2303.04642, arXiv.org.
  85. Daniela Firoiu & George H. Ionescu & Cerasela Pîrvu & Ramona Pîrvu & Cristian Mihai Cismaș & Melinda Petronela Costin, 2024. "The Road to 2030: Evaluating Europe’s Progress on Sustainable Ecosystem Protection and Restoration," Land, MDPI, vol. 13(12), pages 1-20, November.
  86. Joel A. Martínez-Regalado & Cinthia Leonora Murillo-Avalos & Purificación Vicente-Galindo & Mónica Jiménez-Hernández & José Luis Vicente-Villardón, 2021. "Using HJ-Biplot and External Logistic Biplot as Machine Learning Methods for Corporate Social Responsibility Practices for Sustainable Development," Mathematics, MDPI, vol. 9(20), pages 1-16, October.
  87. Katsuya Ito & Kentaro Minami & Kentaro Imajo & Kei Nakagawa, 2020. "Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction," Papers 2012.10215, arXiv.org.
  88. Ajith, Meenu & Martínez-Ramón, Manel, 2023. "Deep learning algorithms for very short term solar irradiance forecasting: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
  89. Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2022. "Bayesian Neural Networks for Macroeconomic Analysis," Papers 2211.04752, arXiv.org, revised Apr 2024.
  90. Icaro Romolo Sousa Agostino & Wesley Vieira da Silva & Claudimar Pereira da Veiga & Adriano Mendonça Souza, 2020. "Forecasting models in the manufacturing processes and operations management: Systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1043-1056, November.
  91. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
  92. 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.
  93. Faizal Hafiz & Jan Broekaert & Davide La Torre & Akshya Swain, 2021. "A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting," Papers 2111.08060, arXiv.org.
  94. Xie, Yiwei & Hu, Pingfang & Zhu, Na & Lei, Fei & Xing, Lu & Xu, Linghong & Sun, Qiming, 2020. "A hybrid short-term load forecasting model and its application in ground source heat pump with cooling storage system," Renewable Energy, Elsevier, vol. 161(C), pages 1244-1259.
  95. Pengfei Zhao & Haoren Zhu & Wilfred Siu Hung NG & Dik Lun Lee, 2024. "From GARCH to Neural Network for Volatility Forecast," Papers 2402.06642, arXiv.org.
  96. Mustafa Ozguven & Chong Yan Gao & Mohamed Yacine Si Tayeb, 2021. "The Utilization of Autoregressive Forecasting Models in Strategic Management," International Journal of Science and Business, IJSAB International, vol. 5(7), pages 170-185.
  97. Gürsakal Necmi & Yilmaz Fırat Melih & Uğurlu Erginbay, 2020. "Finding Opportunity Windows in Time Series Data Using the Sliding Window Technique: the Case of Stock Exchanges," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 24(3), pages 1-19, September.
  98. Pekka Koponen & Jussi Ikäheimo & Juha Koskela & Christina Brester & Harri Niska, 2020. "Assessing and Comparing Short Term Load Forecasting Performance," Energies, MDPI, vol. 13(8), pages 1-17, April.
  99. Fabrizio De Caro & Jacopo De Stefani & Gianluca Bontempi & Alfredo A. Vaccaro & Domenico D. Villacci, 2020. "Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons," ULB Institutional Repository 2013/314435, ULB -- Universite Libre de Bruxelles.
  100. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Principles from Experience with Forecasting Competitions," Forecasting, MDPI, vol. 3(1), pages 1-28, February.
  101. Grossman, Irina & Wilson, Tom & Temple, Jeromey, 2023. "Forecasting small area populations with long short-term memory networks," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
  102. 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.
  103. Junyi Lu & Sebastian Meyer, 2020. "Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model," IJERPH, MDPI, vol. 17(4), pages 1-13, February.
  104. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
  105. Henry Ekwaro-Osire & Dennis Bode & Klaus-Dieter Thoben & Jan-Hendrik Ohlendorf, 2022. "Identification of Machine Learning Relevant Energy and Resource Manufacturing Efficiency Levers," Sustainability, MDPI, vol. 14(23), pages 1-19, November.
  106. Robert Stok & Paul Bilokon, 2023. "From Deep Filtering to Deep Econometrics," Papers 2311.06256, arXiv.org.
  107. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
  108. George H. Ionescu & Daniela Firoiu & Andra-Maria Manda & Ramona Pîrvu & Elena Jianu & Maria-Eliza Antoniu, 2024. "Progress towards the 2030 Sustainable Development Goals for EU Urban Communities (SDG11)," Sustainability, MDPI, vol. 16(11), pages 1-21, May.
  109. Prosper Lamothe-Fernández & David Alaminos & Prosper Lamothe-López & Manuel A. Fernández-Gámez, 2020. "Deep Learning Methods for Modeling Bitcoin Price," Mathematics, MDPI, vol. 8(8), pages 1-13, July.
  110. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
  111. 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.
  112. Jennifer L. Castle & Jurgen A. Doornik & David Hendry, 2019. "Some forecasting principles from the M4 competition," Economics Papers 2019-W01, Economics Group, Nuffield College, University of Oxford.
  113. 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.
  114. Huber, Jakob & Stuckenschmidt, Heiner, 2021. "Intraday shelf replenishment decision support for perishable goods," International Journal of Production Economics, Elsevier, vol. 231(C).
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