Reducing Forecast Instability with Global Deep Learning Models

Jente Van Belle, Ruben Crevits and Wouter Verbeke

Foresight: The International Journal of Applied Forecasting, 2023, issue 69, 49-55

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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:for:ijafaa:y:2023:i:69:p:49-55

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