Impact of KEDattention mechanisms on model computational efficiency.

Impact of KEDattention mechanisms on model computational efficiency.

<p>The input length is set to <i>I</i> = 96, and the prediction steps are . The time required for each epoch is used as an indicator of the model’s computational speed.</p>

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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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Summary:	<p>The input length is set to <i>I</i> = 96, and the prediction steps are . The time required for each epoch is used as an indicator of the model’s computational speed.</p>

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