Machine Learning-Based Anomaly Detection for Multivariate Time Series With Correlation Dependency

Abstract

Recent advances in data collection facilitate the acquisition of large quantities of multivariate time series (MTS) data from various real-world systems. Anomaly detection in high-dimensional MTS data is essential to improving the productivity and safety of such systems; however, capturing the complex intercorrelations between different pairs of time series related to anomalous patterns is challenging. In this study, two different anomaly detection problems—mean shift and structural change—were defined based on the correlation dependency of MTS. Existing algorithms were experimentally analyzed and compared based on their correlation dependency encoding methods using synthetic datasets, with the results revealing that the explicit encoding of correlation dependency improves the predictive performance of anomaly detection in MTS data.