A Study on the Effect of Fuzzy Membership Function on Fuzzified RIPPER for Stock Market Prediction

Abstract

The stock market price prediction is a challenging real world problem as the prediction model is trained on data with uncertainties and fluctuations. This paper is an attempt to find a membership function with least error of prediction for a fuzzified RIPPER hybrid model, for stock market prediction. The stock market prices were predicted using a hybrid model of FRBS and RIPPER. Three different membership functions of the FRBS, namely triangle, trapezoidal and Gaussian, are considered in this study. The parameters of this function are designed to predict the stock market prices and then MAPE is calculated to determine the membership function that gives the least error. This hybrid model was used to predict the stock prices of four datasets and the MAPE error was calculated for all the membership functions.