POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection

2026-05-18

AI Summary

This work addresses the challenge in multivariate time series anomaly detection where over-generalized spatial structure modeling leads to erroneous reconstruction of anomalies and reduced recall. To mitigate this, we propose a prior-observation adversarial learning paradigm that jointly optimizes spatiotemporal dependency modeling. Our approach alternately learns an adjacency matrix as a structural prior in the spatial domain and employs a minimax adversarial mechanism to capture the discrepancy between this prior and data-driven observations, thereby significantly enhancing anomaly sensitivity along the temporal dimension and enabling, for the first time, channel-level anomaly localization. To facilitate systematic evaluation, we construct the first synthetic benchmark with precise channel-wise anomaly annotations. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple public datasets as well as our newly introduced benchmark, excelling in both temporal detection and spatial localization tasks.

Abstract

Existing Multivariate Time Series Anomaly Detection (MTSAD) frameworks increasingly rely on integrating Graph Neural Networks (GNNs) with sequence models to capture complex spatio-temporal dependencies. However, less attention is paid to the spatial over-generalization problem, where unconstrained structural modeling indiscriminately reconstructs anomalies, inevitably degrading detection recall. To tackle this problem, we propose a novel framework that unifies spatio-temporal modeling through a joint prior-observation adversarial learning paradigm. In the spatial dimension, the model alternately learns adjacency matrices as structural prior and models the association discrepancy between prior and data-driven observation in a minimax manner during training. Such adversarial optimization not only improves the model sensitivity for time-wise detection, but also enables the model to localize anomalies to specific channels. To systematically evaluate this anomaly localization capability, we further construct a synthetic benchmark equipped with precise channel-wise annotations. Extensive experiments across public datasets and our dedicated benchmark demonstrate that the proposed framework establishes a new state-of-the-art in both time-wise detection and spatial localization tasks. Our code, pre-trained models, and benchmark are publicly available at https://github.com/anocodetest1/POST.

Problem

• spatial over-generalization
• multivariate time series anomaly detection
• anomaly localization
• spatio-temporal dependencies

Innovation

• adversarial learning
• spatio-temporal modeling
• anomaly localization
• graph neural networks
• multivariate time series