Fuzzy autocorrelation model with confidence intervals of fuzzy random data

Yoshiyuki Yabuuchi, Junzo Watada

    Research output: Contribution to journalArticle

    4 Citations (Scopus)

    Abstract

    Economic analyses are typical methods based on timeseries data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis. In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random timeseries data. These confidence intervals play an essential role in dealing with fuzzy random data on the fuzzy autocorrelation model that we have presented. We analyze tick-by-tick data of stock transactions and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model that we propose in this paper.

    Original languageEnglish
    Pages (from-to)197-203
    Number of pages7
    JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
    Volume18
    Issue number2
    Publication statusPublished - 2014 Mar

    Keywords

    • Autocorrelation
    • Confidence intervals
    • Fuzzy random variables
    • Fuzzy time-series model
    • Possibility

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Vision and Pattern Recognition
    • Human-Computer Interaction

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