Distance Measure for Symbolic Approximation Representation with Subsequence Direction for Time Series Data Mining
Tianyu Li, Fang-Yan Dong, and Kaoru Hirota
Department of Computational Intelligence & Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
A distance measure is proposed for time series data mining based on symbolic aggregate approximation (SAX) with direction representation. It aims at increasing lower bound tightness to Euclidean distance and decreasing the error rate of time series data mining tasks by adding the time series subsequence direction factor to original SAX. Experiments on public University of California, Riverside (UCR) time series datasets, which contain various time series data with diverse type, length, and size, demonstrate that the tightness of the proposed distance measure increases 17.54% on average when compared with that of original SAX, and classification error rates on SAX with direction representation are reduced by 16.22% in comparison with that of results obtained by original SAX. The proposed approach lowers the classification error rate and could be applied to other time series data mining tasks, such as clustering, query by content, and motif discovery.
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