AutoLDT: a lightweight spatio-temporal decoupling transformer framework with AutoML method for time series classification

AutoLDT: a lightweight spatio-temporal decoupling transformer framework with AutoML method for time series classification

Abstract Time series classification finds widespread applications in civil, industrial, and military fields, while the classification performance of time series models has been improving with the recent development of deep learning. However, the issues of feature extraction effectiveness, model comp...

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Bibliographic Details
Main Authors: Peng Wang, Ke Wang, Yafei Song, Xiaodan Wang
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Time series classification
TS-separable linear self-attention mechanism
Automated machine learning
Fuzzy position encoding
Covariance matrix adaptation evolution strategy
Online Access:https://doi.org/10.1038/s41598-024-81000-1
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https://doi.org/10.1038/s41598-024-81000-1

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