Detection of change points in time series analysis with fuzzy statistics

Kuldeep Kumar, Berlin Wu

Research output: Contribution to journalArticleResearchpeer-review

18 Citations (Scopus)

Abstract

Changes in time series often occur gradually so that there is a certain amount of fuzziness in the change point. In this paper we have presented an integrated identification procedure for change-point detection based on fuzzy logic. The membership function of each datum corresponding to the cluster centres is calculated and is used for performance index grouping. We have also suggested a test for the change in level and the change in slope for testing a hypothesis about change points. We have made simulation studies to demonstrate the whole procedure. Finally an empirical study about change-point identification in the exchange rate data of six Asian nations has been demonstrated using the algorithm of the paper.

Original languageEnglish
Pages (from-to)1185-1192
Number of pages8
JournalInternational Journal of Systems Science
Volume32
Issue number9
DOIs
Publication statusPublished - 1 Sep 2001

Fingerprint

Fuzzy Statistics
Time series analysis
Change Point
Time Series Analysis
Membership functions
Fuzzy logic
Time series
Statistics
Testing
Change-point Detection
Fuzziness
Exchange rate
Performance Index
Membership Function
Grouping
Empirical Study
Fuzzy Logic
Slope
Simulation Study
Demonstrate

Cite this

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Detection of change points in time series analysis with fuzzy statistics. / Kumar, Kuldeep; Wu, Berlin .

In: International Journal of Systems Science, Vol. 32, No. 9, 01.09.2001, p. 1185-1192.

Research output: Contribution to journalArticleResearchpeer-review

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