Time Series Similar Pattern Matching Based on Empirical Mode Decomposition

Author

Liu, Huiting ; Ni, Zhiwei ; Li, Jianyang

Author_Institution

Inst. of Comput. Network Syst., Hefei Univ. of Technol.

Volume

1

fYear

2006

fDate

16-18 Oct. 2006

Firstpage

644

Lastpage

648

Abstract

Similar pattern matching of sequence is an important field in time series data mining. Since time series may be very long, which results in query performance decreasing sharply when the database is large, therefore, dimension reduction is required before pattern matching. Fourier transform can be used for dimension reduction, but it cannot provide any feature of signals in local interval. According to this situation, a new similar pattern matching method is proposed in this paper. Firstly, trends of time series are extracted by empirical mode decomposition, and the trends are translated into vectors to realize dimension reduction. Secondly, the vectors are clustered by a forward propagation learning algorithm. Finally, all the series that are similar with the query are found by calculating Euclidean distance between the query and the series that belong to the same category with it. Experimental results show that it is an effective pattern-matching algorithm

Keywords

Fourier transforms; data mining; learning (artificial intelligence); pattern matching; time series; Euclidean distance; Fourier transforms; database; dimension reduction; empirical mode decomposition; forward propagation learning algorithm; pattern matching; time series data mining; Back; Clustering algorithms; Computer networks; Data mining; Databases; Feeds; Forward contracts; Fourier transforms; Neural networks; Pattern matching;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on

Conference_Location

Jinan

Print_ISBN

0-7695-2528-8

Type

conf

DOI

10.1109/ISDA.2006.273

Filename

4021515