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.

翻译：多元时间序列是现代关键基础设施的基础，使得异常预测成为主动风险缓解的重要需求。虽然联合嵌入预测架构为建模这些系统的潜在演化提供了一个有前景的框架，但其应用受到表示崩溃和无法捕捉不同时间尺度上的前兆信号的阻碍。为了解决这些局限性，我们提出了MTS-JEPA，这是一种集成了多分辨率预测目标与软码本瓶颈的专用架构。该设计明确地将瞬态冲击与长期趋势解耦，并利用码本来捕捉离散的状态转换。值得注意的是，我们发现这种约束也充当了内在的正则化器，以确保优化稳定性。在标准基准上的实证评估证实，我们的方法有效防止了退化解，并在早期预警协议下实现了最先进的性能。