Design of a non-linear time series prediction model for daily electricity demand forecasting

Non-linear time series prediction has been a challenging task and important area of research in all branches of science and technology. Though several techniques are used for the pattern prediction problem, identifying unknown, valid information such as patterns and relationships from large time series databases is difficult, due to the presence of noise and high dimensionality. Hence a mathematical model is proposed to provide an effective solution for non-linear time series prediction. The advantage of this model is in handling noise and high dimensionality. The experimental comparison of the proposed model with traditional models like auto regressive integrated moving average (ARIMA) and generalised auto regressive conditional heteroskedastic (GARCH) models on different time series datasets has proved that the prediction accuracy of the proposed model is better than the models taken for comparison. In this paper, the comparison results in forecasting the electricity power demand is discussed.