A Total Variation Based Method for Multivariate Time Series Segmentation

Multivariate time series segmentation is an important problem in data min-ing and it has arisen in more and more practical applications in recent years. The task of time series segmentation is to partition a time series into segments by detecting the abrupt changes or anomalies in the time series. Multivariate time series segmen-tation can provide meaningful information for further data analysis, prediction and policy decision. A time series can be considered as a piecewise continuous function, it is natural to take its total variation norm as a prior information of this time series. In this paper, by minimizing the negative log-likelihood function of a time series, we propose a total variation based model for multivariate time series segmentation. An iterative process is applied to solve the proposed model and a search combined the dynamic programming method is designed to determine the breakpoints. The experi-mental results show that the proposed method is efficient for multivariate time series segmentation and it is competitive to the existing methods for multivariate time series segmentation.