通过前向和后向预测建模实现实时主动异常检测
Real-Time Proactive Anomaly Detection via Forward and Backward Forecast Modeling
AI总结：
本文提出前向预测模型和后向重建模型，通过结合TCNs、GRUs和Transformer实现主动异常检测，提升异常预测的及时性和准确性。
摘要：
传统的反应式异常检测方法通常在异常发生后基于观察到的偏差来识别异常，这在需要及时干预的应用中往往表现不足，例如工业监控、金融和网络安全。相比之下，主动式异常检测旨在在故障完全显现之前检测早期预警信号，但现有方法在处理异构多变量数据和在噪声或不可预测条件下保持精度方面存在困难。在本工作中，我们引入了两种主动式异常检测框架：前向预测模型（FFM）和后向重建模型（BRM）。两种模型都利用结合了时间卷积网络（TCNs）、门控循环单元（GRUs）和Transformer编码器的混合架构来建模方向性的时间动态。FFM预测未来序列以预测中断，而BRM从未来上下文中重建近期历史以揭示早期前兆。异常通过预测误差幅度和方向嵌入差异来标记。我们的模型支持连续和离散多变量特征，能够在现实世界中实现稳健的性能。在四个基准数据集（MSL、SMAP、SMD和PSM）上的广泛实验表明，FFM和BRM在检测指标上优于最先进的基线方法，并显著提高了异常预测的及时性。这些特性使我们的方法适合在需要主动监控的时间敏感领域中部署。
英文摘要：
Reactive anomaly detection methods, which are commonly deployed to identify anomalies after they occur based on observed deviations, often fall short in applications that demand timely intervention, such as industrial monitoring, finance, and cybersecurity. Proactive anomaly detection, by contrast, aims to detect early warning signals before failures fully manifest, but existing methods struggle with handling heterogeneous multivariate data and maintaining precision under noisy or unpredictable conditions. In this work, we introduce two proactive anomaly detection frameworks: the Forward Forecasting Model (FFM) and the Backward Reconstruction Model (BRM). Both models leverage a hybrid architecture combining Temporal Convolutional Networks (TCNs), Gated Recurrent Units (GRUs), and Transformer encoders to model directional temporal dynamics. FFM forecasts future sequences to anticipate disruptions, while BRM reconstructs recent history from future context to uncover early precursors. Anomalies are flagged based on forecasting error magnitudes and directional embedding discrepancies. Our models support both continuous and discrete multivariate features, enabling robust performance in real-world settings. Extensive experiments on four benchmark datasets, MSL, SMAP, SMD, and PSM, demonstrate that FFM and BRM outperform state-of-the-art baselines across detection metrics and significantly improve the timeliness of anomaly anticipation. These properties make our approach well-suited for deployment in time-sensitive domains requiring proactive monitoring.
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。
