Abstract
Time series data naturally arise in many application domains, and the similarity search for time series under dynamic time shifting is prevailing. But most recent research focused on the full length similarity match of two time series. In this paper a basic subsequence similarity search algorithm based on dynamic programming is proposed. For a given query time series, the algorithm can find out the most similar subsequence in a long time series. Furthermore two improved algorithms are also given in this paper. They can reduce the computation amount of the distance matrix for subsequence similarity search. Experiments on real and synthetic data sets show that the improved algorithms can significantly reduce the computation amount and running time comparing with the basic algorithm.
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© 2006 Springer-Verlag Berlin Heidelberg
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Liu, B., Xu, J., Wang, Z., Wang, W., Shi, B. (2006). Subsequence Similarity Search Under Time Shifting. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_65
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DOI: https://doi.org/10.1007/11795131_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
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