Nonstationary time series analysis by temporal clustering

Author

Policker, Shai ; Geva, Amir B.

Author_Institution

Dept. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel

Volume

30

Issue

2

fYear

2000

fDate

4/1/2000 12:00:00 AM

Firstpage

339

Lastpage

343

Abstract

The object of this paper is to present a model and a set of algorithms for estimating the parameters of a nonstationary time series generated by a continuous change in regime. We apply fuzzy clustering methods to the task of estimating the continuous drift in the time series distribution and interpret the resulting temporal membership matrix as weights in a time varying, mixture probability distribution function (PDF). We analyze the stopping conditions of the algorithm to infer a novel cluster validity criterion for fuzzy clustering algorithms of temporal patterns. The algorithm performance is demonstrated with three different types of signals

Keywords

fuzzy set theory; pattern clustering; time series; continuous drift; fuzzy clustering; nonstationary time series; temporal clustering; temporal membership matrix; time series analysis; Clustering algorithms; Clustering methods; Data mining; Electroencephalography; Epilepsy; Hidden Markov models; Parameter estimation; Pattern analysis; Probability distribution; Time series analysis;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/3477.836381

Filename

836381