Comparison of classic and novel change point detection methods for time series with changes in variance

Segmentation or change point detection is a very common topic in time series analysis, anomaly detection and pattern recognition. In Breitenberger et al. (2017) the time series generated by sensors with 3D accelerometers were analysed. It was noticed that such series consist of segments of independent and correlated observations. Hence the appropriate methods for change point detection for both data types must be implemented simultaneously. This paper provides an auxiliary comparison analysis which we intend to implement later for the above mentioned acceleration data. The available methods require usually a long execution time, so that it is time-consuming if several methods should be compared. In the framework of the present publication we want to give additional help for detecting a suitable change point detection method and for finding a good parameter setting. Our analysis is performed on simulated time series, that are normally distributed with constant but unknown mean and changes in variance.