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

Yoshiyuki Yabuuchi, Junzo Watada

    研究成果: Chapter

    抜粋

    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.

    元の言語English
    ホスト出版物のタイトルIntelligent Systems Reference Library
    ページ347-367
    ページ数21
    47
    DOI
    出版物ステータスPublished - 2013

    出版物シリーズ

    名前Intelligent Systems Reference Library
    47
    ISSN(印刷物)18684394
    ISSN(電子版)18684408

      フィンガープリント

    ASJC Scopus subject areas

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

    これを引用

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