Application of genetic and fuzzy modelling in time series analysis

Kuldeep Kumar, Berlin Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2 Citations (Scopus)

Abstract

Researchers have proposed several change point detection and testing methods. However, in real cases, it has been shown that the structure of a time series changes gradually, i.e. The change points illustrate a sense of fuzziness. This research is based on the concept of a time series model and on fuzzy theory. It combines the concept of genetic models with other leading models. We use time series statistical models as chromosomes in the process of genetic evolution, and also use the membership functions of selected models as a performance index of the chromosomes. Change point analysis could be helpful in fitting different models to different data regimes. These models could then be used for forecasting future time series data using a genetic algorithm approach instead of using only the last model. Also, different models at different time periods could give some insight regarding an economic interpretation of the data during that regime.

Original languageEnglish
Title of host publicationProceedings - 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages128-132
Number of pages5
ISBN (Electronic)0769503004, 9780769503004
DOIs
Publication statusPublished - 1 Jan 1999
EventThe 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999 - New Delhi, India
Duration: 23 Sep 199926 Sep 1999
Conference number: 3rd

Conference

ConferenceThe 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999
Abbreviated titleICCIMA
CountryIndia
CityNew Delhi
Period23/09/9926/09/99

Fingerprint

Time series analysis
Time series
Chromosomes
Membership functions
Genetic algorithms
Economics
Testing

Cite this

Kumar, K., & Wu, B. (1999). Application of genetic and fuzzy modelling in time series analysis. In Proceedings - 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999 (pp. 128-132). [798515] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCIMA.1999.798515
Kumar, Kuldeep ; Wu, Berlin. / Application of genetic and fuzzy modelling in time series analysis. Proceedings - 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999. IEEE, Institute of Electrical and Electronics Engineers, 1999. pp. 128-132
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Kumar, K & Wu, B 1999, Application of genetic and fuzzy modelling in time series analysis. in Proceedings - 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999., 798515, IEEE, Institute of Electrical and Electronics Engineers, pp. 128-132, The 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999, New Delhi, India, 23/09/99. https://doi.org/10.1109/ICCIMA.1999.798515

Application of genetic and fuzzy modelling in time series analysis. / Kumar, Kuldeep; Wu, Berlin.

Proceedings - 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999. IEEE, Institute of Electrical and Electronics Engineers, 1999. p. 128-132 798515.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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Kumar K, Wu B. Application of genetic and fuzzy modelling in time series analysis. In Proceedings - 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999. IEEE, Institute of Electrical and Electronics Engineers. 1999. p. 128-132. 798515 https://doi.org/10.1109/ICCIMA.1999.798515