Self-Supervised Disentangled Representation Learning for Time Series Anomaly Detection

Anomaly detection is a fundamental component of intelligent monitoring in the Internet of Things (IoT), where accuracy, efficiency, and interpretability are critical requirements. However, existing methods often overlook the unique characteristics of IoT signals such as seasonality, trends, and irregular residual components, as well as the complex interactions among them. This oversight can lead to anomaly masking, increased false positives, and reduced interpretability in anomaly identification. Motivated by the effectiveness of disentangled representation learning, we propose TRAdetector, a novel disentangled reconstruction-based framework for IoT signals anomaly detection. TRAdetector explicitly models recurrent and consistent patterns, as well as irregular variations in the latent space by leveraging variational inference strategies, thereby enhancing probabilistic guidance in learning both regular and irregular temporal representations. A sparse coding strategy is incorporated within the latent space of the residual component to directly model inconsistent temporal fluctuations. Finally, a multihead cross-attention mechanism and a gated, decomposition-aware reconstruction strategy are designed to effectively model the complex interactions among different components. Extensive experiments show that our model achieves state-of-the-art performance on multiple benchmark datasets in terms of accuracy, efficiency, and interpretability.