关键词: 深度学习, 神经网络, 异常检测, 时间序列, 时间序列预测

Abstract: Anomaly detection is a critical component of time series analysis for identifying anomalous events. To address the limitations of traditional methods in integrating spatio-temporal correlations, capturing normal data distributions, and handling time-varying characteristics, we propose a Time Series Anomaly Detection Model Based on Spatiotemporal Feature Fusion and Sequence Reconstruction (AnomNet). This model comprises three main components: a Spatio-Temporal Feature Fusion Network (STF), a Time Series Reconstruction Network (TSR), and an Anomaly Detection Mechanism (ADM). First, the STF combines temporal convolutional networks and graph attention influence networks to capture temporal long-term dependencies and global attribute associations, thereby achieving joint modeling of spatio-temporal features. Subsequently, the TSR employs a multi-layer encoder-decoder architecture, utilizing spatio-temporal fused features and cyclical information to learn the normal distribution of samples, which amplifies the discrepancies between reconstructed data and potential anomalies. Finally, the ADM detects anomalies by fitting the tail distribution of the reconstruction deviations. Once the anomaly score exceeds a predefined threshold, the mechanism triggers an update of the generalized Pareto distribution parameters, providing the latest standards for subsequent detection. The experimental results on five datasets validate that the AnomNet achieves state-of-the-art performance in the field of time series anomaly detection. Compared to OmniAnomaly, the proposed model shows an average performance improvement of 11.89%.

Key words: Deep Learning, Neural Networks, Anomaly Detection, Time Series, Time Series Prediction