KEDformer: Knowledge extraction seasonal trend decomposition for long-term sequence prediction

Zhenkai Qin, Baozhong Wei, Caifeng Gao and Jianyuan Ni

PLOS ONE, 2025, vol. 20, issue 10, 1-23

Abstract: Time series forecasting is essential in energy, finance, and meteorology. However, existing Transformer-based models face challenges with computational inefficiency and poor generalization for long-term sequences. To address these issues, this study proposes the KEDformer framework. It integrates knowledge extraction and seasonal-trend decomposition to optimize model performance. By leveraging sparse attention and autocorrelation, KEDformer reduces computational complexity from O(L2) to O(L log L), enhancing the model’s ability to capture both short-term fluctuations and long-term patterns. Experiments on five public datasets covering energy, transportation, and weather tasks demonstrate that KEDformer consistently outperforms traditional models, with an average improvement of 10.4% in MSE prediction accuracy and 2.9% in MAE prediction accuracy.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0335047

DOI: 10.1371/journal.pone.0335047

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