Detecting and Analyzing Nonstationarity in a Time Series Using Nonlinear Cross Predictions

We propose a test for stationarity in a time series which checks for the compatibility of nonlinear approximations to the dynamics made in different segments of the sequence. The segments are compared directly, rather than via statistical parameters. The approach provides detailed information about episodes with similar dynamics during the measurement period. This allows for a detailed analysis of physically relevant changes in the dynamics.