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

Michael Chaykowsky

Presentation Attendance: Yes, we will present in-person

Keywords: time-series, JEPA, self-supervised Learning, representation learning

TL;DR: TF-JEPA is a noncontrastive self-supervised method using predictive alignment of time-frequency views for multivariate time series, enhancing transfer learning and reducing computational cost versus traditional contrastive methods.

Abstract: 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.

Track: Research Track (max 4 pages)

Submission Number: 14