Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data

Yoshiyuki Yabuuchi, Junzo Watada

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    The objective of economic analysis is to interpret the past, present or future economic state by analyzing economic data. Economic analyses are typically based on the time-series data or the cross-section data. Time-series analysis plays a pivotal role in analyzing time-series data. Nevertheless, economic systems are complex ones because they involve human behaviors and are affected by many factors. When a system includes substantial uncertainty, such as those concerning human behaviors, it is advantageous to employ a fuzzy system approach to such analysis. In this paper, we compare two fuzzy time-series models, namely a fuzzy autoregressive model proposed by Ozawa et al. and a fuzzy autocorrelation model proposed by Yabuuchi andWatada. Both models are built based on the concepts of fuzzy systems. In an analysis of the Nikkei Stock Average, we compare the effectiveness of the two models. Finally, we analyze tick-by-tick data of stock dealing by applying fuzzy autocorrelation model.

    Original languageEnglish
    Title of host publicationIntelligent Systems Reference Library
    Pages347-367
    Number of pages21
    Volume47
    DOIs
    Publication statusPublished - 2013

    Publication series

    NameIntelligent Systems Reference Library
    Volume47
    ISSN (Print)18684394
    ISSN (Electronic)18684408

    Fingerprint

    Autocorrelation
    time series
    Time series
    Economics
    Fuzzy systems
    economics
    Time series analysis
    time series analysis
    Economic analysis
    economic system
    Time series data
    Stock prices
    uncertainty
    present
    Ticks
    Human behavior

    Keywords

    • economic analysis
    • fuzzy AR model
    • fuzzy autocorrelation
    • possibility

    ASJC Scopus subject areas

    • Computer Science(all)
    • Information Systems and Management
    • Library and Information Sciences

    Cite this

    Yabuuchi, Y., & Watada, J. (2013). Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data. In Intelligent Systems Reference Library (Vol. 47, pp. 347-367). (Intelligent Systems Reference Library; Vol. 47). https://doi.org/10.1007/1007/978-3-642-33439-9_16

    Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data. / Yabuuchi, Yoshiyuki; Watada, Junzo.

    Intelligent Systems Reference Library. Vol. 47 2013. p. 347-367 (Intelligent Systems Reference Library; Vol. 47).

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Yabuuchi, Y & Watada, J 2013, Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data. in Intelligent Systems Reference Library. vol. 47, Intelligent Systems Reference Library, vol. 47, pp. 347-367. https://doi.org/10.1007/1007/978-3-642-33439-9_16
    Yabuuchi Y, Watada J. Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data. In Intelligent Systems Reference Library. Vol. 47. 2013. p. 347-367. (Intelligent Systems Reference Library). https://doi.org/10.1007/1007/978-3-642-33439-9_16
    Yabuuchi, Yoshiyuki ; Watada, Junzo. / Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data. Intelligent Systems Reference Library. Vol. 47 2013. pp. 347-367 (Intelligent Systems Reference Library).
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