Nonparametric changepoint detection for time series
Jürgen Franke, Gerald Kroisandt · PAMM · 2003
Abstract We consider time series which change their structure repeatedly between a finite number of states, and we discuss algorithms to detect the changepoints for two particular situations. In the first case, the observed time series is a nonparametric autoregression of order p and the autoregression function changes sometimes. Here, we use a system of neural networks to estimate the autoregression functions and to detect the changepoints. In the second case, the time series is a piecewise linear process with stable innovations, where we assume that the various processes represent different dominating local frequencies, and we use wavelet packet coefficients to detect the changepoints.