CompleMatch: Boosting Time-Series Semi-Supervised Classification With Temporal-Frequency Complementarity

Time series Semi-Supervised Classification (SSC) aims to improve model performance by utilizing abundant unlabeled data in scenarios where labeled samples are limited. Previous approaches mainly focus on exploiting temporal dependencies within the time domain for SSC. However, these temporal dependencies are susceptible to sampling noise and may not effectively capture the global periodicity of features across categories. To this end, we propose a time series SSC framework called CompleMatch, leveraging the complementary information from both temporal and frequency representations for unlabeled data learning. CompleMatch simultaneously trains two deep neural networks based on time-domain and frequency-domain views, with pseudo-labels generated via label propagation in the representation space guiding the training of the opposing view's classifier. In this co-training paradigm, we incorporate a constraint term to harness the complementary nature of temporal-frequency representations, thereby enhancing the model's robustness under limited labeled data. In addition, we design a temporal-frequency contrastive learning module that integrates supervised and self-supervised signals to enhance pseudo-label quality by learning more discriminative representations. Extensive experiments demonstrate that CompleMatch surpasses state-of-the-art methods. Furthermore, analyses of model behavior (i.e., ablation studies and visualization) underscore the effectiveness of our proposed approach.