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.
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References
Basseville, M., Kikiforov, I.: Detection of Abrupt Changes. Printice Hall, Englewood Cliffs (1993)
Kadambe, S., Boudreaux-Bartels, G.: Application of the wavelet transform for pitch detection of speech signals. IEEE Transactions on Information Theory 38(2), 917–924 (1992)
Hirano, S., Tsumoto, S.: Mining similar temporal patterns in long time-series data and its application to medicine. In: ICDM 2002: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), Washington, DC, USA, p. 219. IEEE Computer Society, Los Alamitos (2002)
Gombay, E.: Change detection in autoregressive time series. J. Multivar. Anal. 99(3), 451–464 (2008)
Ide, T., Inoue, K.: Knowledge discovery from heterogeneous dynamic systems using change-point correlations. In: Proc. SIAM Intl. Conf. Data Mining (2005)
Zha, H., Simon, H.D.: On updating problems in latent semantic indexing. SIAM Journal on Scientific Computing 21(2), 782–791 (1999)
Ide, T., Tsuda, K.: Change-point detection using krylov subspace learning. In: Proceedings of the SIAM International Conference on Data Mining (2007)
Moskvina, V., Zhigljavsky, A.: An algorithm based on singular spectrum analysis for change-point detection. Communications in Statistics.Simulation and Computation 32(4), 319–352 (2003)
Mohammad, Y., Xu, Y., Matsumura, K., Nishida, T.: The h 3 r explanation corpus:human-human and base human-robot interaction dataset. In: The fourth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2008) (December 2008)
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Mohammad, Y., Nishida, T. (2009). Robust Singular Spectrum Transform. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_13
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DOI: https://doi.org/10.1007/978-3-642-02568-6_13
Publisher Name: Springer, Berlin, Heidelberg
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