A time series signal anomaly detection method based on graph neural network

Anomaly detection in high-dimensional time series data refers to the process of identifying sample points that deviate from the overall pattern or expected behavior from a multivariate time series. In high-dimensional time series data, the potential correlation between sensors has a significant impact on the performance of prediction or detection tasks. Graph neural networks are a deep model that learn node representations based on node proximity relationships, which can effectively model complex correlations between sensors. However, existing anomaly detection methods based on graph neural networks mostly rely on a single similarity measure to capture the relationships between variables and cannot learn the dependencies between variables well. In addition, when selecting thresholds, existing methods use the maximum anomaly score in normal data as the cutting threshold, which limits the detection ability when abnormal events occur, resulting in lower recall rates. In summary, this paper proposes a temporal signal anomaly detection method based on graph neural networks, which integrates multiple similarity measures based on the unique features of sensors for graph structure learning. Then, the graph structure learning method is combined with the graph neural network to obtain anomaly scores. Finally, the interval search method was used to optimize the F-measure index and find the optimal anomaly cutting threshold. Experiments on two real sensor datasets showed that our method achieved higher F₁ values and recall compared to the benchmark comparison method.