Fuzzy autocorrelation model with confidence intervals of fuzzy random data

Yoshiyuki Yabuuchi, Junzo Watada

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Citations (Scopus)

    Abstract

    Economic analyses are typical methods based on time-series data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis. In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random time-series data. This confidence intervals has an essential role in dealing with fuzzy random data on our fuzzy autocorrelation model which we have presented. We analyze tick-by-tick data of stock dealing and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model which we propose in this paper.

    Original languageEnglish
    Title of host publication6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
    Pages1938-1943
    Number of pages6
    DOIs
    Publication statusPublished - 2012
    Event2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012 - Kobe
    Duration: 2012 Nov 202012 Nov 24

    Other

    Other2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012
    CityKobe
    Period12/11/2012/11/24

    Fingerprint

    Autocorrelation
    Time series
    Economics
    Fuzzy systems

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software

    Cite this

    Yabuuchi, Y., & Watada, J. (2012). Fuzzy autocorrelation model with confidence intervals of fuzzy random data. In 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012 (pp. 1938-1943). [6505212] https://doi.org/10.1109/SCIS-ISIS.2012.6505212

    Fuzzy autocorrelation model with confidence intervals of fuzzy random data. / Yabuuchi, Yoshiyuki; Watada, Junzo.

    6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012. 2012. p. 1938-1943 6505212.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Yabuuchi, Y & Watada, J 2012, Fuzzy autocorrelation model with confidence intervals of fuzzy random data. in 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012., 6505212, pp. 1938-1943, 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012, Kobe, 12/11/20. https://doi.org/10.1109/SCIS-ISIS.2012.6505212
    Yabuuchi Y, Watada J. Fuzzy autocorrelation model with confidence intervals of fuzzy random data. In 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012. 2012. p. 1938-1943. 6505212 https://doi.org/10.1109/SCIS-ISIS.2012.6505212
    Yabuuchi, Yoshiyuki ; Watada, Junzo. / Fuzzy autocorrelation model with confidence intervals of fuzzy random data. 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012. 2012. pp. 1938-1943
    @inproceedings{220c4705bb7947679ae5e8ed790b49f3,
    title = "Fuzzy autocorrelation model with confidence intervals of fuzzy random data",
    abstract = "Economic analyses are typical methods based on time-series data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis. In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random time-series data. This confidence intervals has an essential role in dealing with fuzzy random data on our fuzzy autocorrelation model which we have presented. We analyze tick-by-tick data of stock dealing and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model which we propose in this paper.",
    author = "Yoshiyuki Yabuuchi and Junzo Watada",
    year = "2012",
    doi = "10.1109/SCIS-ISIS.2012.6505212",
    language = "English",
    isbn = "9781467327428",
    pages = "1938--1943",
    booktitle = "6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012",

    }

    TY - GEN

    T1 - Fuzzy autocorrelation model with confidence intervals of fuzzy random data

    AU - Yabuuchi, Yoshiyuki

    AU - Watada, Junzo

    PY - 2012

    Y1 - 2012

    N2 - Economic analyses are typical methods based on time-series data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis. In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random time-series data. This confidence intervals has an essential role in dealing with fuzzy random data on our fuzzy autocorrelation model which we have presented. We analyze tick-by-tick data of stock dealing and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model which we propose in this paper.

    AB - Economic analyses are typical methods based on time-series data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis. In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random time-series data. This confidence intervals has an essential role in dealing with fuzzy random data on our fuzzy autocorrelation model which we have presented. We analyze tick-by-tick data of stock dealing and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model which we propose in this paper.

    UR - http://www.scopus.com/inward/record.url?scp=84877838288&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84877838288&partnerID=8YFLogxK

    U2 - 10.1109/SCIS-ISIS.2012.6505212

    DO - 10.1109/SCIS-ISIS.2012.6505212

    M3 - Conference contribution

    AN - SCOPUS:84877838288

    SN - 9781467327428

    SP - 1938

    EP - 1943

    BT - 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012

    ER -