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M5 accuracy competition: Results, findings, and conclusions

Citations

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Cited by:

  1. Fritsch, Markus & Haupt, Harry & Schnurbus, Joachim, 2025. "Efficiency of poll-based multi-period forecasting systems for German state elections," International Journal of Forecasting, Elsevier, vol. 41(2), pages 670-688.
  2. Fu, Tong & Huang, Dasen & Feng, Lingbing & Tang, Xiaoping, 2024. "More is better? The impact of predictor choice on the INE oil futures volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
  3. Xu Gong & Mengjie Li & Keqin Guan & Chuanwang Sun, 2023. "Climate change attention and carbon futures return prediction," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1261-1288, September.
  4. 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.
  5. van der Haar, Joost F. & Wellens, Arnoud P. & Boute, Robert N. & Basten, Rob J.I., 2024. "Supervised learning for integrated forecasting and inventory control," European Journal of Operational Research, Elsevier, vol. 319(2), pages 573-586.
  6. 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.
  7. Rombouts, Jeroen & Ternes, Marie & Wilms, Ines, 2025. "Cross-temporal forecast reconciliation at digital platforms with machine learning," International Journal of Forecasting, Elsevier, vol. 41(1), pages 321-344.
  8. Yashon O. Ouma & Ditiro B. Moalafhi & George Anderson & Boipuso Nkwae & Phillimon Odirile & Bhagabat P. Parida & Jiaguo Qi, 2022. "Dam Water Level Prediction Using Vector AutoRegression, Random Forest Regression and MLP-ANN Models Based on Land-Use and Climate Factors," Sustainability, MDPI, vol. 14(22), pages 1-31, November.
  9. Long, Xueying & Bui, Quang & Oktavian, Grady & Schmidt, Daniel F. & Bergmeir, Christoph & Godahewa, Rakshitha & Lee, Seong Per & Zhao, Kaifeng & Condylis, Paul, 2025. "Scalable probabilistic forecasting in retail with gradient boosted trees: A practitioner’s approach," International Journal of Production Economics, Elsevier, vol. 279(C).
  10. Pierre Dodin & Jingyi Xiao & Yossiri Adulyasak & Neda Etebari Alamdari & Lea Gauthier & Philippe Grangier & Paul Lemaitre & William L. Hamilton, 2023. "Bombardier Aftermarket Demand Forecast with Machine Learning," Interfaces, INFORMS, vol. 53(6), pages 425-445, November.
  11. Ge, Xianlong & Yin, Qiushuang & Moktadir, Md. Abdul & Ren, Jingzheng, 2025. "Dynamic routing optimization of electric vehicles for retailers based on consumer behavior prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 204(C).
  12. Daniil Koloskov & Marina Turuntseva, 2025. "The oil and coke prices forecast evaluation using the different forecasting scheme," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 80, pages 5-25.
  13. Girolimetto, Daniele & Athanasopoulos, George & Di Fonzo, Tommaso & Hyndman, Rob J., 2024. "Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1134-1151.
  14. Zhang, Bohan & Panagiotelis, Anastasios & Li, Han, 2025. "Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1022-1036.
  15. Schmidt, Felix G. & Pibernik, Richard, 2025. "Data-driven inventory control for large product portfolios: A practical application of prescriptive analytics," European Journal of Operational Research, Elsevier, vol. 322(1), pages 254-269.
  16. 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.
  17. Feddersen, Leif & Cleophas, Catherine, 2026. "Hierarchical neural additive models for interpretable demand forecasts," International Journal of Forecasting, Elsevier, vol. 42(1), pages 216-234.
  18. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
  19. 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.
  20. Stephanie R. Clark & Dan Pagendam & Louise Ryan, 2022. "Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks," IJERPH, MDPI, vol. 19(9), pages 1-31, April.
  21. Cerqueira, Vitor & Torgo, Luis & Bontempi, Gianluca, 2024. "Instance-based meta-learning for conditionally dependent univariate multi-step forecasting," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1507-1520.
  22. Feng, Lingbing & Rao, Haicheng & Lucey, Brian & Zhu, Yiying, 2024. "Volatility forecasting on China's oil futures: New evidence from interpretable ensemble boosting trees," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 1595-1615.
  23. 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.
  24. Marco Zanotti, 2025. "On the stability of global forecasting models," Working Papers 553, University of Milano-Bicocca, Department of Economics.
  25. 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.
  26. Han Su & Xiaojia Guo & Xiaoke Zhang, 2026. "Regularized Ensemble Forecasting for Learning Weights from Historical and Current Forecasts," Papers 2602.11379, arXiv.org.
  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. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024. "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
  29. Jeroen Rombouts & Marie Ternes & Ines Wilms, 2024. "Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning," Papers 2402.09033, arXiv.org, revised May 2024.
  30. Genov, Evgenii & Cauwer, Cedric De & Kriekinge, Gilles Van & Coosemans, Thierry & Messagie, Maarten, 2024. "Forecasting flexibility of charging of electric vehicles: Tree and cluster-based methods," Applied Energy, Elsevier, vol. 353(PA).
  31. Nils Hinrichs & Tobias Roeschl & Pia Lanmueller & Felix Balzer & Carsten Eickhoff & Benjamin O’Brien & Volkmar Falk & Alexander Meyer, 2024. "Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery," PLOS Digital Health, Public Library of Science, vol. 3(9), pages 1-18, September.
  32. de Vilmarest, Joseph & Werge, Nicklas, 2025. "An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1514-1520.
  33. Zhang, Bohan & Panagiotelis, Anastasios & Kang, Yanfei, 2024. "Discrete forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 318(1), pages 143-153.
  34. Bergsma, Ritsaart & de Ruijt, Corné & Bhulai, Sandjai, 2025. "A systematic review of machine learning approaches in inventory control optimization," Operations Research Perspectives, Elsevier, vol. 15(C).
  35. Paul Goodwin & Jim Hoover & Spyros Makridakis & Fotios Petropoulos & Len Tashman, 2023. "Business forecasting methods: Impressive advances, lagging implementation," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-12, December.
  36. Sprangers, Olivier & Wadman, Wander & Schelter, Sebastian & de Rijke, Maarten, 2024. "Hierarchical forecasting at scale," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1689-1700.
  37. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
  38. Daniele Girolimetto & Tommaso Di Fonzo, 2024. "Point and probabilistic forecast reconciliation for general linearly constrained multiple time series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(2), pages 581-607, April.
  39. Zhu, Ziyang & Zheng, Yuhao & Wang, Xinyi & Huang, Dasen & Feng, Lingbing, 2025. "Forecasting China's precious metal futures volatility: GBRT models and time-model dimension combination of Tree SHAP," International Review of Financial Analysis, Elsevier, vol. 104(PA).
  40. Abdelfatah, Omar Sharafeldin Mohamed, 2026. "AI-Driven Demand Forecasting and Its Impact on Inventory Optimization," SocArXiv uw57j_v1, Center for Open Science.
  41. Yannik Hahn & Tristan Langer & Richard Meyes & Tobias Meisen, 2023. "Time Series Dataset Survey for Forecasting with Deep Learning," Forecasting, MDPI, vol. 5(1), pages 1-21, March.
  42. 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.
  43. 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.
  44. 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.
  45. Nabeel Ahmad Saidd, 2026. "A Controlled Comparison of Deep Learning Architectures for Multi-Horizon Financial Forecasting: Evidence from 918 Experiments," Papers 2603.16886, arXiv.org.
  46. Molina-Muñoz, Jesús & Mora-Valencia, Andrés & Perote, Javier, 2025. "Dynamic volatility spillovers among commodities, bitcoin, and emerging markets," Emerging Markets Review, Elsevier, vol. 69(C).
  47. Godahewa, Rakshitha & Bergmeir, Christoph & Erkin Baz, Zeynep & Zhu, Chengjun & Song, Zhangdi & García, Salvador & Benavides, Dario, 2025. "On forecast stability," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1539-1558.
  48. Sagaert, Yves R. & Kourentzes, Nikolaos, 2025. "Inventory management with leading indicator augmented hierarchical forecasts," Omega, Elsevier, vol. 136(C).
  49. Tuominen, Jalmari & Pulkkinen, Eetu & Peltonen, Jaakko & Kanniainen, Juho & Oksala, Niku & Palomäki, Ari & Roine, Antti, 2024. "Forecasting emergency department occupancy with advanced machine learning models and multivariable input," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1410-1420.
  50. Konstantinos-Leonidas Bisdoulis, 2024. "Assets Forecasting with Feature Engineering and Transformation Methods for LightGBM," Papers 2501.07580, arXiv.org.
  51. Konstantinos Oikonomou & Dimitris Damigos, 2025. "Short term forecasting of base metals prices using a LightGBM and a LightGBM - ARIMA ensemble," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 37-49, March.
  52. Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.
  53. Liu, Tianhao & Li, Fangning & Zhang, Dongdong & Shan, Linke & Zhu, Hongyu & Du, Pengcheng & Jiang, Meihui & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Kong, Fannie, 2026. "Intelligent load forecasting technologies for diverse scenarios in the new power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PD).
  54. Jian'an Zhang, 2025. "A Risk-Neutral Neural Operator for Arbitrage-Free SPX-VIX Term Structures," Papers 2511.06451, arXiv.org.
  55. Marco Zanotti, 2025. "The cost of ensembling: is it always worth combining?," Working Papers 554, University of Milano-Bicocca, Department of Economics.
  56. Abolghasemi, Mahdi & Girolimetto, Daniele & Di Fonzo, Tommaso, 2025. "Improving cross-temporal forecasts reconciliation accuracy and utility in energy market," Applied Energy, Elsevier, vol. 394(C).
  57. Sonnleitner, Benedikt & Kourentzes, Nikolaos & Ehrig, Claudia & Pflaum, Alexander, 2025. "Forecasting for optimization in road freight transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 204(C).
  58. Ekaterina Astafyeva & Marina Turuntseva, 2024. "Forecast evaluation improving using the simplest methods of individual forecasts’ combination," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 74, pages 78-103.
  59. Kafa, Nadine & Babai, M. Zied & Klibi, Walid, 2025. "Forecasting mail flow: A hierarchical approach for enhanced societal wellbeing," International Journal of Forecasting, Elsevier, vol. 41(1), pages 51-65.
  60. Jonas Hanetho, 2023. "Deep Policy Gradient Methods in Commodity Markets," Papers 2308.01910, arXiv.org.
  61. Oikonomou, Konstantinos & Damigos, Dimitris & Dimitriou, Dimitrios, 2025. "Globality in the metal markets: Leveraging cross-learning to forecast aluminum and copper prices," Resources Policy, Elsevier, vol. 103(C).
  62. Ye, Lili & Xie, Naiming & Boylan, John E. & Shang, Zhongju, 2024. "Forecasting seasonal demand for retail: A Fourier time-varying grey model," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1467-1485.
  63. Marco Zanotti, 2025. "Do global forecasting models require frequent retraining?," Working Papers 551, University of Milano-Bicocca, Department of Economics.
  64. Alan Dasilva & Helton Saulo & Roberto Vila & Jose A. Fiorucci & Suvra Pal, 2024. "Parametric quantile autoregressive moving average models with exogenous terms," Statistical Papers, Springer, vol. 65(3), pages 1613-1643, May.
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