Abstract
Joining two time series on subsequence correlation provides useful information about the synchronization of the time series. However, finding the exact subsequence which are most correlated is an expensive computational task. Although the current efficient exact method, JOCOR, requires O(n 2 lgn), where n is the length of the time series, it is still very time-consuming even for time series datasets with medium length. In this paper, we propose an approximate method, LCS-JOCOR, in order to reduce the runtime of JOCOR. Our proposed method consists of three steps. First, two original time series are transformed into two corresponding strings by PAA transformation and SAX discretization. Second, we apply an algorithm to efficiently find the longest common substrings (LCS) of two strings. Finally, the resulting LCSs are mapped back to the original time series to find the most correlated subsequence by JOCOR method. In comparison to JOCOR, our proposed method performs much faster while high accuracy is guaranteed.
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Acknowledgement
We would like to thank Mr. John, a member of Matlab forum, for introducing some valuable ideas on the algorithm for finding LCS of two strings.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Vinh, V.D., Chau, N.P., Anh, D.T. (2017). An Efficient Method for Time Series Join on Subsequence Correlation Using Longest Common Substring Algorithm. In: Cong Vinh, P., Tuan Anh, L., Loan, N., Vongdoiwang Siricharoen, W. (eds) Context-Aware Systems and Applications. ICCASA 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 193. Springer, Cham. https://doi.org/10.1007/978-3-319-56357-2_13
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DOI: https://doi.org/10.1007/978-3-319-56357-2_13
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