Learning Disentangled Task-Related Representation for Time Series

Multivariate time series representation learning employs unsupervised tasks to extract meaningful representations from time series data, enabling their application in diverse downstream tasks. However, despite the promising advancements in contrastive learning-based representation learning, the study of task-related feature learning is still in its early stages. This gap exists because current unified representation learning frameworks lack the ability to effectively disentangle task-related features. To address this limitation, we propose DisT, a novel contrastive learning-based method for efficient task-related feature learning in time series representation. DisT disentangles task-related features by incorporating feature network structure learning and contrastive sample pair selection. Specifically, DisT incorporates a feature decoupling module, which prioritizes global features for time series classification tasks, while emphasizing periodic and seasonal features for forecasting tasks. Additionally, DisT leverages contrastive loss and task-related feature loss to adaptively select data augmentation methods, preserving task-relevant shared information between positive samples across different data and tasks. Experimental results on various multivariate time-series datasets including classification and forecasting tasks show that DisT achieves state-of-the-art performance.