抄録
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.
本文言語 | English |
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ページ(範囲) | 166-172 |
ページ数 | 7 |
ジャーナル | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
巻 | 3215 |
出版ステータス | Published - 2004 |
ASJC Scopus subject areas
- コンピュータ サイエンス(全般)
- 生化学、遺伝学、分子生物学(全般)
- 理論的コンピュータサイエンス