Multivariate time series anomaly detection via dynamic graph attention network and Informer.

In the industrial Internet, industrial software plays a central role in enhancing the level of intelligent manufacturing. It enables the promotion of digital collaborative services. Effective anomaly detection of multivariate time series can ensure the quality of industrial software. Extensive research has been conducted on time series anomaly detection to identify abnormal data. However, detecting anomalies in multivariate time series, which consist of high-dimensional, high-noise, and random data, poses significant challenges. The states of different timestamps within a time series sample can influence the overall correlation of sensor features. Unfortunately, existing methods often overlook this impact, making it difficult to capture subtle variations in the delayed response of attacked sensors.Consequently, there are false alarms and abnormal omissions. To address these limitations, this paper proposes an anomaly detection method called DGINet. DGINet leverages a dynamic graph attention network and Informer to capture and integrate feature correlation across different time states. By combining GRU and Informer, DGINet effectively captures continuous correlations in long time series. Moreover, DGINet simultaneously optimizes the reconstruction and forecasting modules, enhancing its overall performance. Experimental results on four benchmark datasets demonstrate that DGINet outperforms state-of-the-art methods by achieving up to a 2 % improvement in accuracy. Further analysis reveals that DGINet excels in accurately detecting anomalies in long time series and locating candidate abnormal attack points. [ABSTRACT FROM AUTHOR]