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Time Series Analysis, Modeling and Applications pp 347–367Cite as

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

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 47))

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.

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Author information

Authors and Affiliations

  1. Faculty of Economics, Shimonoseki City University, 2-1-1 Daigaku-cho, Shimonoseki, Yamaguchi, 751-8510, Japan

    Yoshiyuki Yabuuchi

  2. Graduate School of Information, Production and Systems, Waseda University, 2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan

    Junzo Watada

Authors
  1. Yoshiyuki Yabuuchi
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  2. Junzo Watada
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Corresponding author

Correspondence to Yoshiyuki Yabuuchi .

Editor information

Editors and Affiliations

  1. , Electrical & Computer Engineering, University of Alberta, 9107 - 116 Street, 2nd Floor, ECERF, Edmonton, T6G 2V4, Canada

    Witold Pedrycz

  2. , Graduate Institute of Educational Measur, National Taichung University of Educatio, Administration Building A110, Taichung, Taiwan R.O.C.

    Shyi-Ming Chen

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Yabuuchi, Y., Watada, J. (2013). Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data. In: Pedrycz, W., Chen, SM. (eds) Time Series Analysis, Modeling and Applications. Intelligent Systems Reference Library, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33439-9_16

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  • DOI: https://doi.org/10.1007/978-3-642-33439-9_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33438-2

  • Online ISBN: 978-3-642-33439-9

  • eBook Packages: EngineeringEngineering (R0)

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