Regression Trees for change point analysis: methods, applications and recent developments

The detection of change points in time series is a popular subject of research and the most challenging task is to identify multiple changes occurring at unknown date. At this aim [2] proposed a method called ART (Atheoretical Regression Trees) that employs least squares regression trees to detect multiple breaks in the mean. In this paper we present a review of the method as well as two recent extensions meant to deal either with changes in the coefficients of a parametric model or with changes in time series imprecisely observed i.e. time ordered observations whose values are not known exactly, such as interval or ordinal time series. An empirical example concerning a continuous-valued imprecise time series is presented and discussed.