Building a Rough Sets-Based Prediction Model of Tick-Wise Stock Price Fluctuations

Yoshiyuki Matsumoto, Junzo Watada

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Rough sets enable us to mine knowledge in the form of IF-THEN decision rules from a data repository, a database, a web base, and others. Decision rules are used to reason, estimate, evaluate, and forecast. The objective of this paper is to build the rough sets-based model for analysis of time series data with tick-wise price fluctuations where knowledge granules are mined from the data set of tickwise price fluctuations. We show how a method based on rough sets helps acquire the knowledge from time-series data. The method enables us to obtain IF-THEN type rules for forecasting stock prices.

    Original languageEnglish
    Title of host publicationIntelligent Systems Reference Library
    Pages301-329
    Number of pages29
    Volume47
    DOIs
    Publication statusPublished - 2013

    Publication series

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

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    ASJC Scopus subject areas

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

    Cite this

    Matsumoto, Y., & Watada, J. (2013). Building a Rough Sets-Based Prediction Model of Tick-Wise Stock Price Fluctuations. In Intelligent Systems Reference Library (Vol. 47, pp. 301-329). (Intelligent Systems Reference Library; Vol. 47). https://doi.org/10.1007/1007/978-3-642-33439-9_14