Chaotic time series prediction with a global model: Artificial neural network

Abstract

An investigation on the performance of artificial neural network (ANN) as a global model over the widely used local models (local averaging technique and local polynomials technique) in chaotic time series prediction is conducted. A theoretical noise-free chaotic time series, a noise added theoretical chaotic time series and two chaotic river flow time series are analyzed in this study. Three prediction horizons (1, 3 and 5 lead times) are considered. A limited number of parameter combinations were considered to select the best ANN models (MLPs) for prediction. This procedure was shown to be effective at least for the time series considered in this study. A remarkable prediction performance was gained with Global ANN models on noise-free chaotic Lorenz series. The overall results showed the superiority of global ANN models over the widely used local prediction models.