HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions
This work addresses the problem of maintaining normal operation in complex systems by improving anomaly detection accuracy for multivariate time series, representing an incremental advancement in the field.
The paper tackles anomaly detection in multivariate time series by addressing the lack of correlation modeling among variables, proposing HIFI, which uses graph convolutional networks and attention mechanisms to achieve high-order feature interactions, and demonstrates superiority over state-of-the-art methods on three public datasets.
Monitoring complex systems results in massive multivariate time series data, and anomaly detection of these data is very important to maintain the normal operation of the systems. Despite the recent emergence of a large number of anomaly detection algorithms for multivariate time series, most of them ignore the correlation modeling among multivariate, which can often lead to poor anomaly detection results. In this work, we propose a novel anomaly detection model for multivariate time series with \underline{HI}gh-order \underline{F}eature \underline{I}nteractions (HIFI). More specifically, HIFI builds multivariate feature interaction graph automatically and uses the graph convolutional neural network to achieve high-order feature interactions, in which the long-term temporal dependencies are modeled by attention mechanisms and a variational encoding technique is utilized to improve the model performance and robustness. Extensive experiments on three publicly available datasets demonstrate the superiority of our framework compared with state-of-the-art approaches.