Trend and Order Features for Semi-Supervised Time-Series Classification via Multitask Learning

Multitask learning with a pretext task has excelled in time-series classification task lacking labeled data. The key to multitask learning is to build a pretext task and learn the most representative feature from the raw time series. In this article, we propose trend and order features for semi-supervised time-series classification via multitask learning (TOFL). Specifically, we propose a simple but effective pretext task-self-sequence order prediction (SOP)-to discover the order relation. In addition, we design a gradual trend fusion (GTF) block concatenating different trend features as the shared backbone network basis element to obtain high-quality trend features for the SOP task. Finally, we not only theoretically analyze the uniform stability and generalization error of TOFL but also evaluate the results compared with state-of-the-art (SOTA) supervised and semi-supervised methods on the 128 UCR datasets and three real-world datasets. TOFL demonstrates a high level of competitiveness and, in most cases, closely matches or even surpasses SOTA methods in terms of accuracy. The source code and data of TOFL are freely available at: https://github.com/Sample-design-alt/TOFL.