KEDformer: Knowledge extraction seasonal trend decomposition for long-term sequence prediction - Zhenkai Qin; Baozhong Wei; Caifeng Gao; Jianyuan Ni (PLoS ONE, 2025)
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(L 2 ) 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.