HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Entropy Causal Graphs for Multivariate Time Series Anomaly Detection
Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between variables in multivariate time series da…
Anomaly DetectionTime SeriesTime Series Anomaly DetectionGenAD: General Representations of Multivariate Time Series for Anomaly Detection
Anomaly Detection(AD) for multivariate time series is an active area in machine learning, with critical applications in Information Technology system management, Spacecraft Health monitoring, Multi-Robot Systems detectio…
Anomaly DetectionManagementTime SeriesTime Series Analysis+1Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection
We proposed a multivariate time series anomaly detection frame-work Ymir, which leverages ensemble learning and supervisedlearning technology to efficiently learn and adapt to anomaliesin real-world system applications. …
Anomaly DetectionEnsemble LearningTime SeriesTime Series Analysis+2Algorithmic Recourse for Anomaly Detection in Multivariate Time Series
Anomaly detection in multivariate time series has received extensive study due to the wide spectrum of applications. An anomaly in multivariate time series usually indicates a critical event, such as a system fault or an…
Anomaly DetectionTime SeriesTime Series Anomaly DetectionRoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series
A multivariate time series refers to observations of two or more variables taken from a device or a system simultaneously over time. There is an increasing need to monitor multivariate time series and detect anomalies in…
Anomaly DetectionTime SeriesTime Series Anomaly Detection