Enhanced accuracy of fuzzy time series predictor using genetic algorithm

Abstract

Accuracy is one of the most important aspects in the domain of forecasting. It is very difficult to improve accuracy of prediction system where prediction is based only on large historical values and accuracy is important for each predicted value along with the whole system. The main objective of this research is to optimize dominant factors of fuzzy time series predictor (FTSP) using genetic algorithm (GA) and further to improve prediction accuracy for each time series variable along with whole system. This is obtained by (a) generating wide range of parameters for membership function at time t on the basis of their base value (b) subset of population generated at time t is used for fitness checking. Additionally, GA complexity is also reduced by utilizing rate of change of time series data to cut down the bit size of chromosome. It can be observed from comparative study that use of GA considerably reduced mean square error (MSE) and average forecasting error rate (AFER).