Research on similarity matching for multiple granularities time-series data

Authors:
Wenning Hao;Enlai Zhao;Hongjun Zhang;Gang Chen;Dawei Jin
Affiliations:
Engineering Institue of Corps of Engineers, PLA University of Science & Technology, Nanjing, China;Engineering Institue of Corps of Engineers, PLA University of Science & Technology, Nanjing, China;Engineering Institue of Corps of Engineers, PLA University of Science & Technology, Nanjing, China;Engineering Institue of Corps of Engineers, PLA University of Science & Technology, Nanjing, China;Engineering Institue of Corps of Engineers, PLA University of Science & Technology, Nanjing, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

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Abstract

Because of the appropriate algorithm of measuring multiple granularities time-series is few, this article advanced a similarity matching algorithm for multiple granularities time-series, which based on the ideal of time calibrator and hypothesis test. It firstly expounded the definition of multiple granularities time-series, and proposed a sample of distance; secondly, it put forward the similarity matching algorithm of multiple granularities time-series; finally, the experimental result proved that the algorithm can effectively reflect the time-series of multiple granularities.