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Rough Set Model Based Knowledge Acquisition of Market Movements from Economic Data

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Information Granularity, Big Data, and Computational Intelligence

Part of the book series: Studies in Big Data ((SBD,volume 8))

Abstract

The concept and method of rough sets were proposed by Z. Pawlak in 1982. This method enables us to mine knowledge granules as decision rules from a database, a web base, a set and so on. The obtained decision rules can be applicable for data analysis as well as used to reason, estimate, evaluate, or forecast an unknown object. The objective of this paper is to apply the rough set method to time series data for mining knowledge granules, and especially to mine knowledge granules from the data set of tick-wise price fluctuations.

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Author information

Authors and Affiliations

  1. Shimonoseki City University, 2-1-1, Daigaku-Cho, Shimonoseki, Yamaguchi, 751-8510, Japan

    Yoshiyuki Matsumoto

  2. Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan

    Junzo Watada

Authors
  1. Yoshiyuki Matsumoto
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  2. Junzo Watada
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Correspondence to Junzo Watada .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Matsumoto, Y., Watada, J. (2015). Rough Set Model Based Knowledge Acquisition of Market Movements from Economic Data. In: Pedrycz, W., Chen, SM. (eds) Information Granularity, Big Data, and Computational Intelligence. Studies in Big Data, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-08254-7_18

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  • DOI: https://doi.org/10.1007/978-3-319-08254-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08253-0

  • Online ISBN: 978-3-319-08254-7

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