A proposal of chaotic forecasting method based on wavelet transform

Yoshiyuki Matsumoto, Junzo Watada

    研究成果: Article査読


    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.

    ジャーナルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    出版ステータスPublished - 2004

    ASJC Scopus subject areas

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

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