Neural Network Reproduction of Time Series Data with Varying Amplitudes and Frequencies

Abstract

An artificial neural network simulation was applied to the recognition and reproduction of time series data whose amplitudes and frequencies simultaneously change with time. The model is composed of two neural networks respectively predicting the change of amplitudes and frequencies. The results of our model were compared with those obtained by the least squares method using four kinds of model functions. Our model gives higher quality results than the least squares method especially in the prediction of amplitude change.