Real-Time Proactive Anomaly Detection via Forward and Backward Forecast Modeling

TL;DR

This paper introduces two innovative proactive anomaly detection frameworks, FFM and BRM, that leverage advanced neural architectures to identify early warning signals in multivariate time series data, outperforming existing reactive methods in timeliness and accuracy.

Contribution

The paper presents novel proactive anomaly detection models combining TCNs, GRUs, and Transformers, capable of handling heterogeneous data and improving early detection in noisy environments.

Findings

Outperform state-of-the-art baselines on four benchmark datasets.

Significantly improve timeliness of anomaly detection.

Robust performance with heterogeneous multivariate data.

Abstract

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.