A proposal of chaotic forecasting method based on wavelet transform

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.