TF-JEPA: Predictive Alignment of Time–Frequency Representations Without Contrastive Pairs

Learning generalizable representations from multivariate time series is challenging due to complex temporal dynamics, distribution shifts, and the difficulty of effectively designing contrastive pairs. We introduce TF-JEPA, a noncontrastive self-supervised method that leverages predictive alignment to integrate representations from the time and frequency domains without relying on negative sampling. TF-JEPA utilizes dual online time and frequency encoders, each paired with its own momentum-updated target encoder, embedding both views into a stable and unified latent space. Experiments on sleep EEG, gesture recognition, mechanical fault detection, and EMG classification demonstrate that TF-JEPA matches or surpasses contrastive and time frequency consistency baselines.