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<div><p>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 framewo...

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Bibliographic Details
Main Author:	Zhenkai Qin (18778053) (author)
Other Authors:	Baozhong Wei (22493375) (author), Caifeng Gao (3906880) (author), Jianyuan Ni (22493378) (author)
Published:	2025
Subjects:	Science Policy Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified weather tasks demonstrate time series forecasting mse prediction accuracy leveraging sparse attention integrates knowledge extraction optimize model performance div >< p model ’ trend decomposition term sequences term patterns term fluctuations study proposes poor generalization existing transformer computational inefficiency average improvement
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Ablation results.

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