Time series classification algorithm based on residual shrinkage network

Time:2021-5-11

Time series data are vulnerable to interferenceDisturbance of noiseTherefore, the ability to process the noise in time series will have an important impact on the classification accuracy [1].

Time series classification algorithm based on residual shrinkage network

Residual shrinkage network [2] is a classification algorithm for strong noise and high redundancy data, which was first proposed by M. Zhao in reference [2].

Time series classification algorithm based on residual shrinkage network

[1] Zhang Ya Wen. Research on time series classification algorithm based on residual network [D]. Beijing Jiaotong University, 2020
[2]M. Zhao, S. Zhong, X. Fu, B. Tang, M. Pecht, Deep residual shrinkage networks for fault diagnosis, IEEE Transactions on Industrial Informatics, vol. 16, no. 7, pp. 4681-4690, 2020.
[3]https://github.com/zhao62/Deep-Residual-Shrinkage-Networks