Improving the global fitting method on nonlinear time series analysis

We are concerned with improving the forecast capabilities of the global approach to time series. We assume that the normal techniques of global mapping are applied, the noise reduction is performed, etc. Then, using the mathematical foundations behind such approaches, we propose a method that, without a great computational cost, greatly increases the accuracy of the corresponding forecasting.