Robust Singular Spectrum Transform

  • Authors:

  • Yasser Mohammad;Toyoaki Nishida

  • Affiliations:

  • Nishida-Sumi Laboratory, Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Japan;Nishida-Sumi Laboratory, Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Japan

  • Venue:
  • IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
  • Year:
  • 2009

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Abstract

Change Point Discovery is a basic algorithm needed in many time series mining applications including rule discovery, motif discovery, casual analysis, etc. Several techniques for change point discovery have been suggested including wavelet analysis, cosine transforms, CUMSUM, and Singular Spectrum Transform. Of these methods Singular Spectrum Transform (SST) have received much attention because of its generality and because it does not require ad-hoc adjustment for every time series. In this paper we show that traditional SST suffers from two major problems: the need to specify five parameters and the rapid reduction in the specificity with increased noise levels. In this paper we define the Robust Singular Spectrum Transform (RSST) that alleviates both of these problems and compare it to RSST using different synthetic and real-world data series.