Marcelo C. Medeiros, Jeronymo M. Pinro
arXiv 28 Aug 2025 · Econometrics
arXiv:2508.20795 · PDF · DOI · OpenAlex · Extracted main text
The forecasting combination puzzle is a well-known phenomenon in forecasting literature, stressing the challenge of outperforming the simple average when aggregating forecasts from diverse methods. This study proposes a Reinforcement Learning - based framework as a dynamic model selection approach to address this puzzle. Our framework is evaluated through extensive forecasting exercises using simulated and real data. Specifically, we analyze the M4 Competition dataset and the Survey of Professional Forecasters (SPF). This research introduces an adaptable methodology for selecting and combining forecasts under uncertainty, offering a promising advancement in resolving the forecasting combination puzzle.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | John M Bates and Clive WJ Granger (1969) The combination of forecasts | 0.405 | 1 | 1 | 100% |
| 2 | Casper Solheim Bojer and Jens Peder Meldgaard (2021) Kaggle forecasting competitions: An overlooked learning opportunity | 0.405 | 1 | 1 | 100% |
| 3 | Robert T Clemen (1989) Combining forecasts: A review and annotated bibliography | 0.405 | 1 | 1 | 100% |
| 4 | Yunlong Dong, Xiuchuan Tang, and Ye Yuan (2020) Principled reward shaping for reinforcement learning via lyapunov stability theory | 0.405 | 1 | 1 | 100% |
| 5 | Dhivya Elavarasan and PM Durairaj Vincent (2020) Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications | 0.405 | 1 | 1 | 100% |
| 6 | Cong Feng and Jie Zhang (2019) Reinforcement learning based dynamic model selection for short-term load forecasting | 0.405 | 1 | 1 | 100% |
| 7 | Brevin Franklin, Emily Silcock, Abhishek Arora, Tom Bryan, and Melis… (2024) News deja vu: Connecting past and present with semantic search | 0.405 | 1 | 1 | 100% |
| 8 | Shenggong Ji, Zhaoyuan Wang, Tianrui Li, and Yu Zheng (2020) Spatio-temporal feature fusion for dynamic taxi route recommendation via deep reinforcement learning | 0.405 | 1 | 1 | 100% |
| 9 | Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos (2020) The m4 competition: 100,000 time series and 61 forecasting methods | 0.405 | 1 | 1 | 100% |
| 10 | Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos (2022) M5 accuracy competition: Results, findings, and conclusions | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 13 scored citations.