SGFM: Conditional Flow Matching for Time Series Anomaly Detection With State Space Models

The industrial Internet of Things (IoT) landscape is enriched with a diverse array of sensors, which are configured for the real-time monitoring and data collection to improve the production efficiency and optimizing industrial processes. The continual monitoring of IoT systems allows for promptly detection of anomalies in these time series data, thereby minimizing economic losses and ensuring the safe operation of the overall system. Existing deep learning-based methods for time series anomaly detection often rely on the generative models to learn the normal behavior of the data. However, these methods face challenges related to the speed and quality of data generation, ultimately impacting the overall detection performance of the models. In response to these challenges, this article proposes a new unsupervised anomaly detection method named SGFM. It combines state space models and graph neural networks to extract complex spatiotemporal dependencies in time series, which then serve as guidance to facilitate the learning process of a flow matching model, aiming for more refined predictions and consequently enhancing the effectiveness of anomaly detection. The effectiveness of SGFM is validated through the experiments on the three classic time series data sets. The results exhibits a notable improvement of up to 4% compared to the existing methods.