iTimes: Investigating Semisupervised Time Series Classification via Irregular Time Sampling

Semi-supervised learning (SSL) provides a powerful paradigm to mitigate the reliance on large labeled data by leveraging unlabeled data during model training. However, for time series data, few SSL models focus on the underlying temporal structure of time series, which results in a suboptimal repres...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2023-05, Vol.19 (5), p.6930-6938
Main Authors: Liu, Xuxin, Zhang, Fengbin, Liu, Han, Fan, Haoyi
Format: Article
Language:English
Subjects:
Classification
Data models
Informatics
Irregular time sampling
Modules
Representations
Sampling
Self-supervised learning
Semi-supervised learning
semi-supervised learning (SSL)
Semisupervised learning
Task analysis
Time series
Time series analysis
time series classification
Training
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Summary:Semi-supervised learning (SSL) provides a powerful paradigm to mitigate the reliance on large labeled data by leveraging unlabeled data during model training. However, for time series data, few SSL models focus on the underlying temporal structure of time series, which results in a suboptimal representation learning quality on unlabeled time series. In this article, we propose a framework of semisupervised time series classification by investigating irregular time sampling ( iTimes ), which learns the underlying temporal structure of unlabeled time series in a self-supervised manner to benefit semisupervised time series classification. Specifically, we propose four different irregular time sampling functions to transform the original time series into different transformations. Then, iTimes employs a supervised module to classify labeled time series directly and employs a self-supervised module on unlabeled time series by predicting the transformation type of irregular time sampling. Finally, the underlying temporal structure pattern of unlabeled time series can be captured in the self-supervised module. The feature spaces between labeled data and unlabeled data can be aligned by jointly training the supervised and self-supervised modules which boost the ability of model learning and the representation quality. Extensive experimental results on multiple real-world datasets demonstrate the effectiveness of iTimes compared with the state-of-the-art baselines.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3199374