On a Model Approach to Forecasting of Chaotic Time Series
H. Kidachi · Progress of Theoretical Physics · 2000
In order to study forecasting of chaotic time series, artificial chaotic time series that are derived from difference transformations on time series generated by deterministic maps such as the tent map and the logistic map are theoretically analyzed. In order to predict future values of these model time series, we need to know past data in addition to the current data. Here, model time series in which the amount of data that is necessary for prediction is definitely determined and a model time series in which, depending on past data, the amount would be indefinitely large are proposed. Both of these models should be useful models of chaotic time series when we test the effectiveness of various forecasting methods that have been and will be proposed.