Chaotic forecasting method based on Wavelet Transform

Issue title: Innovational Soft Computing

Article type: Research Article

Authors: Matsumoto, Yoshiyuki^{a; *} | Watada, Junzo^b

Affiliations: [a] Shimonoseki City University, 2-1-1 Daigakumachi, Shimonoseki, Yamaguchi 751-8510, Japan | [b] Waseda University, 2-2, Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0135, Japan

Correspondence: [*] Corresponding author. Tel.: +81 0832 54 0288; Fax: +81 0832 52 8099; E-mail: [email protected]

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.

DOI: 10.3233/KES-2006-10105

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 10, no. 1, pp. 49-56, 2006