A proposal of chaotic forecasting method based on wavelet transform

Yoshiyuki Matsumoto, Junzo Watada

    Research output: Contribution to journalArticle

    Abstract

    Recently, the chaotic method is employed to forecast a short-term future using uncertain data. This method makes it possible to restructure the attractor of given time-series data in the multi-dimensional space through Takens' embedding theory. However, some time-series data have less chaotic characteristic. In this paper, Time-series data are divided using Wavelet Transform. It will be shown that the divided orthogonal elements of time-series data are employed to forecast more precisely than original time-series data. The divided orthogonal time-series data are forecasted using Chaos method. Forecasted data are restored to the original data by inverse wavelet transform.

    Original languageEnglish
    Pages (from-to)166-172
    Number of pages7
    JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3215
    Publication statusPublished - 2004

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    Wavelet Analysis
    Time Series Data
    Wavelet transforms
    Wavelet Transform
    Forecasting
    Time series
    Forecast
    Uncertain Data
    Inverse transforms
    Chaos theory
    Attractor
    Chaos

    ASJC Scopus subject areas

    • Computer Science(all)
    • Biochemistry, Genetics and Molecular Biology(all)
    • Theoretical Computer Science

    Cite this

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