HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions

TL;DR

HIFI introduces a novel anomaly detection model for multivariate time series that captures high-order feature interactions and long-term dependencies, improving detection accuracy in complex systems.

Contribution

The paper proposes a new model that automatically constructs feature interaction graphs and employs graph convolutional networks with attention and variational encoding for enhanced anomaly detection.

Findings

Outperforms existing methods on three benchmark datasets.

Effectively models high-order feature interactions and temporal dependencies.

Demonstrates robustness and superior accuracy in anomaly detection.

Abstract

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.