Detection of change points in time series analysis with fuzzy statistics

Kuldeep Kumar*, Berlin Wu

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

20 Citations (Scopus)


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
Issue number9
Publication statusPublished - 1 Sept 2001


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