MTS-JEPA：多分辨率联合嵌入预测架构用于时间序列异常预测
SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Prediction
AI总结：
MTS-JEPA通过多分辨率预测目标和软代码本瓶颈，解决时间序列异常预测中的表示崩溃和多时间尺度信号捕捉问题，实现先进性能。
摘要：
多变量时间序列支撑现代关键基础设施，使异常预测成为主动风险缓解的必要需求。虽然联合嵌入预测架构（JEPA）提供了一个有前景的框架来建模这些系统的潜在演变，但其应用受到表示崩溃和无法在不同时间尺度上捕捉前兆信号的限制。为了解决这些限制，我们提出了MTS-JEPA，一种专门的架构，集成了多分辨率预测目标和软代码本瓶颈。该设计明确将短暂冲击与长期趋势分开，并利用代码本捕捉离散状态转换。值得注意的是，我们发现这种约束也起到内在正则化的作用，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，并在预警协议下实现了最先进的性能。
英文摘要：
Time-series anomaly prediction aims to forecast future system failures before they fully emerge, making latent predictive models such as JEPA a promising framework for capturing precursor dynamics. However, directly applying continuous self-distillation to time-series data is often unstable and can lead to representation collapse, while also struggling to model precursors evolving at different temporal scales. To address this, we propose \textbf{SC-JEPA}, a new JEPA-based framework to model time-series anomaly prediction in a discretized predictive state space. It introduces a soft codebook bottleneck to stabilize latent predictive learning and encourage regime-level structure in the learned representations. Building on this stabilized latent space, we further design a multi-resolution predictive objective to capture precursor patterns at different temporal scales. Experiments on five real-world benchmarks show that SC-JEPA achieves strong and consistent early-warning performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
