College of Computer & Information, HoHai University, Nanjing 211100, China

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Abstract—In this paper, we evaluate some techniques for the time series similarity searching. Many distance measures have been proposed as alternatives to the Euclidean distance in the similarity searching. To verify the assumption that the combination of various similarity measures may produce more accurate similarity searching results, we propose an multi-measure algorithm to combine several measures based on weighted BORDA voting method. The proposed method is validated by the analysis results of the flood data obtained from Wangjiaba in the Huaihe basin of China.

Index Terms—multi-measure, similarity searching, hydrological, BORDA voting, time series

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Cite: Jimin Wang, Yuelong Zhu, Dingsheng Wan, Pengcheng Zhang, Jun Feng, "Multi-measure Similarity Searching for Time Series," Journal of Computers vol. 9, no. 10, pp. 2266-2273, 2014.