STDiff: Anomaly Detection over Multivariate Time Series in Autonomous Systems for Industry 5.0 via Spatio-Temporal Learning and Diffusion Generation

In autonomous and adaptive Industry 5.0 systems, sensors on various devices continuously generate high-throughput time series data that are characterized by intensive concurrent requests, high-frequency sampling, and multisource heterogeneity. These complex and large-scale data streams are prone to containing various anomalies caused by noise interference, equipment faults, or operational issues. Therefore, robust and accurate anomaly detection techniques are essential for ensuring the stability and safety of autonomous and adaptive systems. Multivariate time series anomaly detection tasks in autonomous Industry 5.0 systems face two major challenges: insufficient representations of spatio-temporal dependencies and the inadequate detection of diverse anomaly patterns. To address these challenges, we propose an unsupervised anomaly detection framework for multivariate time series that involves Spatio-Temporal Learning and Diffusion Generation (STDiff). First, the Denoising Diffusion Probabilistic Model (DDPM) is adopted as the underlying architecture. The local features of the given time series are learned by multiscale spatio-temporal convolution, while a linear attention mechanism is incorporated to analyze long-term time series. Second, given the long-term context-dependent nature of multivariate time series, we propose a Temporal Feature Memory (TFM) that integrates the memory features encoded at multiple scales with the decoded features. It enhances the ability of the model to preserve long-term temporal patterns. Third, we design a Spatial Interaction Awareness (SIA) for multisource and heterogeneous industrial time series in autonomous systems. It adaptively learns the inter-variable correlations within multivariate time series through Graph Attention Networks (GATs), and the spatial feature representation is enhanced by residual connections. Finally, experimental results obtained on multiple real-world industrial datasets show that the proposed method outperforms the baseline methods and exhibits excellent anomaly detection performance.