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Seasonal Decomposition‐Enhanced Deep Learning Architecture for Probabilistic Forecasting

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  • Keyan Jin
  • Francisco Javier Blanco‐Encomienda

Abstract

Time series decomposition as a general preprocessing method has been widely used in the field of time series forecasting. However, since the future is unknown, this preprocessing practice is limited in realistic forecasting, especially in real‐time forecasting scenarios. In this paper, we propose a framework with time series decomposition and probabilistic forecasting capabilities. Distinguishing from models based on time series pre‐decomposition, our proposed framework can decompose the series into trend components and seasonal components in real time to achieve end‐to‐end forecasting. We apply this framework to four state‐of‐the‐art deep time series models and test their performance on four synthetic datasets and the WTI oil price dataset. The results show that the seasonal decomposition‐based framework can significantly improve the point and probabilistic forecasting accuracy of the original models.

Suggested Citation

  • Keyan Jin & Francisco Javier Blanco‐Encomienda, 2026. "Seasonal Decomposition‐Enhanced Deep Learning Architecture for Probabilistic Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(2), pages 880-891, March.
    • Handle: RePEc:wly:jforec:v:45:y:2026:i:2:p:880-891
    DOI: 10.1002/for.70065
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    References listed on IDEAS

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