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GoodsForecast second-place solution in M5 Uncertainty track: Combining heterogeneous models for a quantile estimation task

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  • Mamonov, Nikolay
  • Golubyatnikov, Evgeny
  • Kanevskiy, Daniel
  • Gusakov, Igor

Abstract

GoodsForecast’s predictions obtained a second place in the M5 Uncertainty Competition. This result was achieved using an empirical method that estimates the probability distribution of time series by combining the forecasts of three methods. The first method considers past observations, estimating their histogram, the second method is used to estimate trends through a singular spectrum analysis, and the last one captures external factors with the help of a machine learning model. Forecasts made by these methods at different levels of aggregation were judgmentally combined to account for the particularities of the M5 data set. Quantiles, estimated by the combination of algorithms, were modified with rounding heuristics that resulted in the improvement of the competition target loss metric. This paper is devoted to a detailed description of the developed algorithmic scheme for evaluating the probability distribution, the experiments, and some insights gained by the authors during the problem- solving process.

Suggested Citation

  • Mamonov, Nikolay & Golubyatnikov, Evgeny & Kanevskiy, Daniel & Gusakov, Igor, 2022. "GoodsForecast second-place solution in M5 Uncertainty track: Combining heterogeneous models for a quantile estimation task," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1434-1441.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:4:p:1434-1441
    DOI: 10.1016/j.ijforecast.2022.04.003
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    References listed on IDEAS

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    1. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207.
    2. Hyndman, Rob J., 2020. "A brief history of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 36(1), pages 7-14.
    3. Barrow, Devon K. & Kourentzes, Nikolaos, 2016. "Distributions of forecasting errors of forecast combinations: Implications for inventory management," International Journal of Production Economics, Elsevier, vol. 177(C), pages 24-33.
    4. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    5. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Quantile forecast optimal combination to enhance safety stock estimation," International Journal of Forecasting, Elsevier, vol. 35(1), pages 239-250.
    6. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Empirical safety stock estimation based on kernel and GARCH models," Omega, Elsevier, vol. 84(C), pages 199-211.
    7. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    8. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207, April.
    9. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    10. John E. Tyworth & Liam O'Neill, 1997. "Robustness of the normal approximation of lead‐time demand in a distribution setting," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(2), pages 165-186, March.
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