提出SC-JEPA模型，提升时序异常预测的稳定性与早期预警能力。
SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Prediction
- 用软码本瓶颈稳定潜在预测学习，防止表征坍缩。
- 多分辨率预测目标捕捉不同时间尺度的异常前兆。
- 在5个真实数据集上表现稳定，适合工业早期预警场景。
时序异常预测旨在系统故障完全显现前进行预警，基于潜在预测的模型如JEPA能有效捕捉前兆动态。然而，直接对时序数据应用连续自蒸馏易引发不稳定性，导致表征坍缩，且难以建模不同时间尺度的前兆演化。为此，我们提出基于JEPA的新型框架SC-JEPA，通过离散化预测状态空间建模时序异常预测。引入软码本瓶颈以稳定潜在预测学习，并促进学习表征中出现层级结构。在此稳定潜在空间基础上，进一步设计多分辨率预测目标，以捕捉不同时间尺度的前兆模式。在五个真实世界基准上的实验表明，SC-JEPA实现了强大且一致的早期预警性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。