Semi-Supervised Time Series Classification By Temporal Relation Prediction

Semi-supervised learning has proven to be a powerful algorithm in different domains by leveraging unlabeled data to mitigate the reliance on the tremendous annotated data. However, few efforts consider the underlying temporal relation structure of unlabeled time series data in the semi-supervised learning paradigm. In this work, we propose a simple and effective method of semi-supervised time series classification architecture (termed as SemiTime) by gaining from the structure of unlabeled data in a self-supervised manner. Specifically, for the labeled time series, SemiTime conducts the supervised classification directly under the supervision of the annotated class label. For the unlabeled time series, the segments of past-future pair are sampled from time series, where two segments of pair from the same time series candidate are in positive temporal relation, while two segments from the different candidates are in negative temporal relation. Then, the temporal relation between those segments is predicted by SemiTime in a self-supervised manner. Finally, by jointly classifying labeled data and predicting the temporal relation of unlabeled data, the useful representation of unlabeled time series can be captured by SemiTime. Extensive experiments on multiple real-world datasets show that SemiTime consistently outperforms the state-of-the-arts, which demonstrates the effectiveness of the proposed method.