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Correlated daily time series and forecasting in the M4 competition

Author

Listed:
  • Ingel, Anti
  • Shahroudi, Novin
  • Kängsepp, Markus
  • Tättar, Andre
  • Komisarenko, Viacheslav
  • Kull, Meelis

Abstract

We participated in the M4 competition for time series forecasting and here describe our methods for forecasting daily time series. We used an ensemble of five statistical forecasting methods and a method that we refer to as the correlator. Our retrospective analysis using the ground truth values published by the M4 organisers after the competition demonstrates that the correlator was responsible for most of our gains over the naïve constant forecasting method. We identify data leakage as one reason for its success, due partly to test data selected from different time intervals, and partly to quality issues with the original time series. We suggest that future forecasting competitions should provide actual dates for the time series so that some of these leakages could be avoided by participants.

Suggested Citation

  • Ingel, Anti & Shahroudi, Novin & Kängsepp, Markus & Tättar, Andre & Komisarenko, Viacheslav & Kull, Meelis, 2020. "Correlated daily time series and forecasting in the M4 competition," International Journal of Forecasting, Elsevier, vol. 36(1), pages 121-128.
    • Handle: RePEc:eee:intfor:v:36:y:2020:i:1:p:121-128
    DOI: 10.1016/j.ijforecast.2019.02.018
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    References listed on IDEAS

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    7. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2018. "The M4 Competition: Results, findings, conclusion and way forward," International Journal of Forecasting, Elsevier, vol. 34(4), pages 802-808.
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    Cited by:

    1. Talagala, Thiyanga S. & Li, Feng & Kang, Yanfei, 2022. "FFORMPP: Feature-based forecast model performance prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 920-943.
    2. 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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