Comparative study of stock market forecasting using different functional link artificial neural networks

摘要

This paper presents different forecasting functional link artificial neural network (FLANN) models to investigate and compare various time series stock data. The architecture of several FLANN models like CFLANN, LFLANN, LeF-LANN, and CEFLANN are discussed. The processing technique and experimental results are provided to investigate the prediction of stocks. This piece of work presents the training and testing of all the models by analysing and forecasting different Indian stocks like IBM, RIL and DWSG. All the forecasting models have been tested using same duration time of time series data. The experimental results illustrate that the trigonometric polynomial-based CEFLANN model outperforms the forecasting time series stock data in terms of percentage average error than the polynomial-based FLANN models. Lastly, the percentage of average error is further improved by optimising the free parameters of the trigonometric polynomial-based CEFLANN model with differential evolution algorithm (DEA).