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.
