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Weighted ensemble of statistical models

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  • Pawlikowski, Maciej
  • Chorowska, Agata

Abstract

We present a detailed description of our submission for the M4 forecasting competition, in which it ranked 3rd overall. Our solution utilizes several commonly used statistical models, which are weighted according to their performance on historical data. We cluster series within each type of frequency with respect to the existence of trend and seasonality. Every class of series is assigned a different set of models to combine. Combination weights are chosen separately for each series. We conduct experiments with a holdout set to manually pick pools of models that perform best for a given series type, as well as to choose the combination approaches.

Suggested Citation

  • Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    • Handle: RePEc:eee:intfor:v:36:y:2020:i:1:p:93-97
    DOI: 10.1016/j.ijforecast.2019.03.019
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    2. 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.
    3. In, YeonJun & Jung, Jae-Yoon, 2022. "Simple averaging of direct and recursive forecasts via partial pooling using machine learning," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1386-1399.
    4. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
    5. Tasquia Mizan & Sharareh Taghipour, 2021. "A causal model for short‐term time series analysis to predict incoming Medicare workload," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 228-242, March.
    6. Meng, Anbo & Chen, Shu & Ou, Zuhong & Xiao, Jianhua & Zhang, Jianfeng & Chen, Shun & Zhang, Zheng & Liang, Ruduo & Zhang, Zhan & Xian, Zikang & Wang, Chenen & Yin, Hao & Yan, Baiping, 2022. "A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network," Energy, Elsevier, vol. 261(PA).
    7. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    8. Godahewa, Rakshitha & Bergmeir, Christoph & Webb, Geoffrey I. & Montero-Manso, Pablo, 2023. "An accurate and fully-automated ensemble model for weekly time series forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 641-658.
    9. Liang, Weifang & Liu, Yong & Somogyi, Simon & Anderson, David P., 2024. "A Multi-Model, Ensemble Approach to Forecasting United States Food Prices," 2024 Annual Meeting, July 28-30, New Orleans, LA 343687, Agricultural and Applied Economics Association.
    10. 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.
    11. Wilson, Tom & Grossman, Irina & Temple, Jeromey, 2023. "Evaluation of the best M4 competition methods for small area population forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 110-122.
    12. 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.

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