Level change detection in time series using higher order statistics

Authors:
C. S. Hilas;I. T. Rekanos;S. K. Goudos;P. A. Mastorocostas;J. N. Sahalos
Affiliations:
Department of Informatics and Communications, Technological Educational Institute of Serres, Greece;Physics Division, School of Engineering, Aristotle University of Thessaloniki, Greece;Radiocommunications Laboratory, Aristotle University of Thessaloniki, Greece;Department of Informatics and Communications, Technological Educational Institute of Serres, Greece;Radiocommunications Laboratory, Aristotle University of Thessaloniki, Greece
Venue:
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Year:
2009

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Abstract

Changes in the level of a time series are usually attributed to an intervention that interrupts its evolution. The resulting time series are referred to as interrupted time series and they are studied in order to measure, e.g. the impact of new laws or medical treatments. In the present paper a heuristic method for level change detection in non-stationary time series is presented. The method uses higher order statistics, namely the skewness and the kurtosis, and can identify both the existence of a change in the level of the time series as well as the time point it has happened. The technique is tested with both simulated and real world data and is straightforward applicable to the detection of outliers in time series.