Multivariate time-series anomaly detection via temporal convolutional and graph attention networks

Multivariate time series anomaly detection has been investigated extensively in recent years. Capturing long-term time series information is one of the challenges in this field. We propose a novel multivariate time series anomaly detection framework MTAD-TCGA comprising several modules that efficiently and accurately capture dependencies in long-term multivariate time series. The proposed model contains a temporal convolutional module and uses two parallel graph attention layers to learn the complex dependencies of time series in both the temporal and feature dimensions. A Gated Recurrent Unit layer, based on an improved attention mechanism, and an auto-regressive model is used for prediction, and the prediction model and reconstruction model are jointly optimized. Finally, the threshold is selected by extreme value theory, and then anomalies are identified. The experimental results on three public datasets show our framework is superior to other state-of-the-art models, achieving F1 scores uniformly at levels above 0.9, verifying the effectiveness and feasibility of the MTAD-TCGA method.