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

Yoshiyuki Yabuuchi*, Junzo Watada

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapter


    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
    Number of pages21
    Publication statusPublished - 2013

    Publication series

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


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

    ASJC Scopus subject areas

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


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