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Change Detection and Data Segmentation Methods

The problem of change detection and data segmentation has received considerable attention in a research context and appears to be the central issue in various application areas. The change detection and segmentation model used in this paper is the simplest extension of the linear regression models to data with abruptly chang- ing properties. Usually, a change detection algorithm consists in two stages: residual generation and decision making. The residuals are analytical redundancy generated data representing the difference between the observed and expected system behavior. In the stage of decision making, the residuals are processed and analyzed under certain decision rules to determine the system change status. The following techniques are investigated: filtering techniques with a whiteness test, techniques based on sliding windows and distance measures, and maximum like- lihood techniques for change point estimation. The results of some Monte-Carlo simulations for change detection and segmentation in signals with changes in the mean value and in the AR model parameters are presented.