LAUSR

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.