Title: Comparative study of stock market forecasting using different functional link artificial neural networks
Authors: Dwiti Krishna Bebarta; Birendra Biswal; P.K. Dash
Addresses: Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, 532127, Srikakulam, AP, India. ' Department of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, 532127, Srikakulam, AP, India. ' Multidisciplinary Research Cell, Siksha O Anusandhan University, Khandagiri Square, Bhubaneswar, 751030, Odisha, India
Abstract: 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).
Keywords: functional link ANNs; artificial neural networks; FLANN; Chebysheb FLANN; Laguerre FLANN; Legendre FLANN; computationally efficient FLANN; differential evolution; stock market forecasting; stock markets.
International Journal of Data Analysis Techniques and Strategies, 2012 Vol.4 No.4, pp.398 - 427
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 16 Nov 2012 *