An Adaptive Forecasting of Nonlinear Nonstationary Time Series under Short Learning Samples

Abstract. Methods of nonstationary nonlinear time series forecasting under bounded a priori information provide an interdisciplinary applications area that is concerned with learning and adaptation of solutions from a traditional artificial intelligence point of view. It is extremely difficult to solve this type of problems in its general form, therefore, an approach based on the additive nonlinear auto regressive model with exogenous inputs and implemented on the base of parallel adalines set has been proposed. To find optimal combination of forecasts, an improvement of global random search has been suggested