Many papers have been presented on the study of change points detection. Nonetheless, we would like to point out that in dealing with the time series with switching regimes, we should also take the characteristics of change periods into account. Because many patterns of change structure in time series exhibit a certain kind of duration, those phenomena should not be treated as a mere sudden turning at a certain time. In this paper, we propose a procedure about change periods detection for nonlinear time series. The detecting statistical method is an application of fuzzy classification and a generalization of Inclan and Tiao's result [J. Am. Statist. Assoc. 89 (1994) 913]. Simulation results show that the performance of the proposed procedure is efficient and successful. Finally, an empirical application about change periods detecting for Taiwan monthly visitor's arrival is demonstrated.