Nonparametric data segmentation in multivariate time series via joint characteristic functions

Modern time series data often exhibit complex dependence and structural changes which are not easily characterized by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series. By considering joint characteristic functions between the time series and its lagged values, our proposed method is able to detect changepoints in the marginal distribution, but also those in possibly nonlinear serial dependence, all without the need to prespecify the type of changes. We show the theoretical consistency of our method in estimating the total number and the locations of the changepoints, and demonstrate its good performance against a variety of changepoint scenarios. We further demonstrate its usefulness in applications to seismology and economic time series.

​AbstractModern time series data often exhibit complex dependence and structural changes which are not easily characterized by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series. By considering joint characteristic functions between the time series and its lagged values, our proposed method is able to detect changepoints in the marginal distribution, but also those in possibly nonlinear serial dependence, all without the need to prespecify the type of changes. We show the theoretical consistency of our method in estimating the total number and the locations of the changepoints, and demonstrate its good performance against a variety of changepoint scenarios. We further demonstrate its usefulness in applications to seismology and economic time series. Read More