Wuhan Univ. J. Nat. Sci.
Volume 27, Number 6, December 2022
|Page(s)||521 - 530|
|Published online||10 January 2023|
CLC number: TP 391
Self-Supervised Time Series Classification Based on LSTM and Contrastive Transformer
School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
† To whom correspondence should be addressed. E-mail: email@example.com
Time series data has attached extensive attention as multi-domain data, but it is difficult to analyze due to its high dimension and few labels. Self-supervised representation learning provides an effective way for processing such data. Considering the frequency domain features of the time series data itself and the contextual feature in the classification task, this paper proposes an unsupervised Long Short-Term Memory (LSTM) and contrastive transformer-based time series representation model using contrastive learning. Firstly, transforming data with frequency domain-based augmentation increases the ability to represent features in the frequency domain. Secondly, the encoder module with three layers of LSTM and convolution maps the augmented data to the latent space and calculates the temporal loss with a contrastive transformer module and contextual loss. Finally, after self-supervised training, the representation vector of the original data can be got from the pre-trained encoder. Our model achieves satisfied performances on Human Activity Recognition (HAR) and sleepEDF real-life datasets.
Key words: self-supervised learning / contrastive learning / time series classification
Biography: ZOU Yuanhao, male, Master candidate, research direction: cloud manufacturing resource combination. E-mail: firstname.lastname@example.org
Supported by the National Key Research and Development Program of China (2019YFB1706401)
© Wuhan University 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.