Intelligent processing of time series using neuro-fuzzy adaptive genetic approach

Abstract

An intelligent approach is proposed for processing of time series based on a neuro-fuzzy network and an adaptive genetic algorithm (AGA). A chaotic time series data is used for network training because the trained network should be applied for forecasting of chaotic time series. A simple technique is used to measure the convergence speed of the GA, which in turn determines the probability values of genetic operators in each generation. Using the adaptive versions of probability values of genetic operators the modified GA version has improved its convergence towards the desired fitness function. As the accuracy measure of the forecast the performance indices such as sum square error (SSE), mean square error (MSE), and mean absolute error (MAE) are used. It was shown that the proposed intelligent approach is an excellent tool for forecasting the chaotic time series.