Time series prediction using a hybrid model of neural network and FIR filter

Abstract

Time series prediction is a very important technology in a wide variety of fields. The actual time series contains both linear and nonlinear properties. The amplitude of the time series to be predicted is usually a continuous value. For this reason, we combine nonlinear and linear predictors in a cascade form. In order to estimate the minimum size of the proposed predictor, we propose a nonlinearity analysis for the time series of interest. Computer simulations using sunspot data have demonstrated the efficiency of the proposed predictor and the nonlinearity analysis.