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Kaggle forecasting competitions: An overlooked learning opportunity

Citations

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Cited by:

  1. Wenjia Wang & Yi-Hui Zhou, 2022. "A Double Penalty Model for Ensemble Learning," Mathematics, MDPI, vol. 10(23), pages 1-23, November.
  2. Sidrah Mumtaz & Mudassar Raza & Ofonime Dominic Okon & Saeed Ur Rehman & Adham E. Ragab & Hafiz Tayyab Rauf, 2023. "A Hybrid Framework for Detection and Analysis of Leaf Blight Using Guava Leaves Imaging," Agriculture, MDPI, vol. 13(3), pages 1-22, March.
  3. 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).
  4. 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.
  5. Lu, Qing-Long & Mahajan, Vishal & Lyu, Cheng & Antoniou, Constantinos, 2024. "Analyzing the impact of fare-free public transport policies on crowding patterns at stations using crowdsensing data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
  6. Milton Soto‐Ferrari, 2025. "Integrating Google Mobility Indices for Forecasting Infectious Diseases Incidence: A Multi‐Country Study on COVID‐19 With LightGBM," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(8), pages 2405-2424, December.
  7. Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2025. "MLOps Monitoring at Scale for Digital Platforms," Papers 2504.16789, arXiv.org.
  8. Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
  9. Naudé, Wim & Bray, Amy & Lee, Celina, 2021. "Crowdsourcing Artificial Intelligence in Africa: Findings from a Machine Learning Contest," IZA Discussion Papers 14545, IZA Network @ LISER.
  10. 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.
  11. Drechsler, Marius & Eiglsperger, Josef & Grimm, Dominik & Holzapfel, Andreas, 2025. "Procurement and production planning in horticulture considering short-term re-order opportunities," International Journal of Production Economics, Elsevier, vol. 284(C).
  12. 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.
  13. Bojer, Casper Solheim, 2022. "Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1555-1561.
  14. Hajkowicz, Stefan & Naughtin, Claire & Sanderson, Conrad & Schleiger, Emma & Karimi, Sarvnaz & Bratanova, Alexandra & Bednarz, Tomasz, 2022. "Artificial intelligence for science – adoption trends and future development pathways," MPRA Paper 115464, University Library of Munich, Germany.
  15. Marcelo C. Medeiros & Jeronymo M. Pinro, 2025. "Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach," Papers 2508.20795, arXiv.org.
  16. 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.
  17. Wen, Qianyun & Liu, Yang, 2025. "Feature engineering and selection for prosumer electricity consumption and production forecasting: A comprehensive framework," Applied Energy, Elsevier, vol. 381(C).
  18. 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.
  19. Cervellera, Cristiano, 2023. "Optimized ensemble value function approximation for dynamic programming," European Journal of Operational Research, Elsevier, vol. 309(2), pages 719-730.
  20. 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.
  21. Makridakis, Spyros & Spiliotis, Evangelos & Hollyman, Ross & Petropoulos, Fotios & Swanson, Norman & Gaba, Anil, 2025. "The M6 forecasting competition: Bridging the gap between forecasting and investment decisions," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1315-1354.
  22. 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.
  23. Oleksandr Shchur & Abdul Fatir Ansari & Caner Turkmen & Lorenzo Stella & Nick Erickson & Pablo Guerron-Quintana & Michael Bohlke-Schneider & Yuyang Wang, 2025. "fev-bench: A Realistic Benchmark for Time Series Forecasting," Boston College Working Papers in Economics 1101, Boston College Department of Economics.
  24. 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).
  25. 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.
  26. 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).
  27. 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.
  28. Li, Libo & Yu, Huan & Kunc, Martin, 2024. "The impact of forum content on data science open innovation performance: A system dynamics-based causal machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
  29. Pierre Pinson & Mikkel Bjørn & Simon Kristiansen & Claus B. Nielsen & Lasse Janerka & Jesper Skovgaard & Kristian Durhuus, 2025. "Data-Driven at Sea: Forecasting and Revenue Management at Molslinjen," Interfaces, INFORMS, vol. 55(1), pages 5-21, January.
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