A space-embedding strategy for anomaly detection in multivariate time series

Abstract

Anomaly detection of time series has always been a hot topic in academia and industry. However, many existing multivariant time series methods suffer from common challenges due to increased dimensionality. In this study, we developed a space-embedding strategy for anomaly detection in multivariant time series (SES-AD). As a hybrid model, SES-AD did not directly search the discords from the original time series, but projected the raw sequence to a lower dimensional space so that the significant abrupt change points in the new space can be easily captured from the dissimilarity vector. Finally, the potential abnormities were determined by a statistical strategy. To verify the performance of our method, SES-AD was applied to an extensive number of multivariate time series. The experimental results suggest that SES-AD is more efficient than five existing approaches. Overall, the SES-AD model is suitable for solving anomaly detection for high-dimensional datasets and guarantees computational effectiveness and accuracy.