Short-term Prediction by Chaos Method of Embedding Related Data at the Same Time
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- MATSUMOTO Yoshiyuki
- Tohwa University
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- WATADA Junzo
- Osaka Institute of Technology
Bibliographic Information
- Other Title
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- 関連データを同時に埋め込んだカオスによる短期予測に関する研究
- カンレン データ オ ドウジ ニ ウメコンダ カオス ニヨル タンキ ヨソク
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Abstract
Recently, the chaotic method is employed to forecast a short-term future using uncertain data. This method is feasible by restructuring the attractor of given time-series data in the multi-dimensional space through Takens' embedding theory. Nevertheless, it is hard to obtain data which comes only from a chaotic source. Ordinarily, many uncertain time-series data do not come only from a chaotic source, but also from another source. In this paper, we employ related information in order to remove the influence of the non-chaotic source from the given data. This method makes forecasting precision higher because the chaotic portion of the given data can be easily abstracted. In the end, the effectiveness and usefulness of our method are shown by application to a short-term forecasting simulation of Nikkei mean data of the Tokyo stock market.
Journal
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- Journal of Japan Industrial Management Association
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Journal of Japan Industrial Management Association 49 (4), 209-217, 1998
Japan Industrial Management Association
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Keywords
Details 詳細情報について
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- CRID
- 1390001205504281216
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- NII Article ID
- 110003945593
- 10012129982
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- NII Book ID
- AN10561806
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- ISSN
- 21879079
- 13422618
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- NDL BIB ID
- 4591141
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- CiNii Articles
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- Abstract License Flag
- Disallowed