Level change detection in time series using higher order statistics

C. S. Hilas, I. T. Rekanos, S. K. Goudos, P. A. Mastorocostas, J. N. Sahalos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationDSP 2009
Subtitle of host publication16th International Conference on Digital Signal Processing, Proceedings
DOIs
Publication statusPublished - 2009
EventDSP 2009:16th International Conference on Digital Signal Processing - Santorini, Greece
Duration: 5 Jul 20097 Jul 2009

Other

OtherDSP 2009:16th International Conference on Digital Signal Processing
CountryGreece
CitySantorini
Period5/07/097/07/09

Keywords

  • Change point detection
  • Heuristic methods
  • Higher order statistics
  • Interrupted time series

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    Hilas, C. S., Rekanos, I. T., Goudos, S. K., Mastorocostas, P. A., & Sahalos, J. N. (2009). Level change detection in time series using higher order statistics. In DSP 2009: 16th International Conference on Digital Signal Processing, Proceedings [5201260] https://doi.org/10.1109/ICDSP.2009.5201260