MTS-JEPA: Multi-Resolution Joint-Embedding Predictive Architecture for Time-Series Anomaly Prediction
This addresses proactive risk mitigation for critical infrastructure through time-series anomaly prediction, representing a domain-specific incremental improvement.
The paper tackles the problem of predicting anomalies in multivariate time series by addressing representation collapse and multi-scale precursor signal capture limitations in Joint-Embedding Predictive Architectures (JEPA). It proposes MTS-JEPA with a multi-resolution predictive objective and soft codebook bottleneck, achieving state-of-the-art performance on standard benchmarks under early-warning protocols.
Multivariate time series underpin modern critical infrastructure, making the prediction of anomalies a vital necessity for proactive risk mitigation. While Joint-Embedding Predictive Architectures (JEPA) offer a promising framework for modeling the latent evolution of these systems, their application is hindered by representation collapse and an inability to capture precursor signals across varying temporal scales. To address these limitations, we propose MTS-JEPA, a specialized architecture that integrates a multi-resolution predictive objective with a soft codebook bottleneck. This design explicitly decouples transient shocks from long-term trends, and utilizes the codebook to capture discrete regime transitions. Notably, we find this constraint also acts as an intrinsic regularizer to ensure optimization stability. Empirical evaluations on standard benchmarks confirm that our approach effectively prevents degenerate solutions and achieves state-of-the-art performance under the early-warning protocol.