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Reducing Forecast Instability with Global Deep Learning Models

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  • Jente Van Belle
  • Ruben Crevits
  • Wouter Verbeke

Abstract

Based on their research published in the International Journal of Forecasting, the authors provide the takeaways that will be of most use to forecasting practitioners. Their approach shows how to reduce forecast instability with global deep learning models without necessarily harming forecast accuracy. This is important for business forecasters, since more stable demand forecasts lead to fewer (and smaller) supply chain plan changes and thus lower supply chain costs.

Suggested Citation

  • Jente Van Belle & Ruben Crevits & Wouter Verbeke, 2023. "Reducing Forecast Instability with Global Deep Learning Models," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 69, pages 49-55, Q2.
    • Handle: RePEc:for:ijafaa:y:2023:i:69:p:49-55
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