Long Short-Term Multivariate Time Series Anomaly Detection via Double-Branch Attention and Dynamic Graph Attention Network

In the industrial Internet, intelligent operation and maintenance can ensure the secure and stable operation of industrial software. To achieve effective intelligent operation and maintenance, it’s essential to conduct time series anomaly detection within software systems and other devices. To enhance the detection effectiveness, extensive research has been conducted. However, multivariate time series are composed of high-dimensional, high-noise, and random data. These anomalies are both subtle and dense. It is difficult to detect anomalies accurately. The increased complexity of existing methods results in lower operational efficiency. To address these issues, this paper proposes the time series anomaly detection method DG-LSFNet. DG-LSFNet adeptly captures feature correlations across various temporal states to extract additional valuable insights. Next, DG-LSFNet establishes long-term and short-term temporal correlations. It can capture normal information within short intervals between anomalies, thereby reducing false alarm in anomaly detection. Then, DG-LSFNet approximate and unearth the original data information to improve anomaly detection performance. In addition, DG-LSFNet reduces time complexity by simplifying the model structure and effectively improves the efficiency of anomaly detection. This paper conducts experiments to compare DG-LSFNet with state-of-the-art methods on four benchmark datasets. The experimental results indicate that DG-LSFNet outperforms state-of-the-art methods in terms of anomaly detection performance, enhancing the interpretability of anomaly detection.