Score Network with Adaptive Augmentation Aggregator for Multivariate Time Series Representation Contrastive Learning

The complexity of multichannel data, the intricate temporal dynamics, and the diverse frequency characteristics of time series pose significant challenges for self-supervised representation learning. To address these issues, we present the Teacher Student Score (TSS) framework, a novel contrastive learning approach for multidimensional time series representations. This framework introduces two key innovations. First, we present time-channel-frequency consistency (TCF-C) approach of time, channel, and frequency-based contrastive representations and incorporate it into contrastive learning framework. This technique utilizes a weighting mechanism to prioritize self-supervised tasks that emphasize consistency across these dimensions. Second, we propose a Score Network with Adaptive Augmentation Aggregator (AAA) module. This module dynamically combines augmented strategies to create a unified augmented representation, enhancing the efficacy of augmentation in contrastive learning. We evaluate our method on UEA datasets against eight state-of-the-art methods, and the results show that TSS achieves significant improvements over existing SOTAs of self-supervised learning for time series classification.