Polynomial Based Functional Link Artificial Recurrent Neural Network adaptive System for predicting Indian Stocks
- DOI
- 10.1080/18756891.2015.1099910How to use a DOI?
- Keywords
- PFLARNN, Polynomial functions, backpropagation learning algorithm, differential evolution, IBM stock indices, MAPE, AMAPE
- Abstract
A low complexity Polynomial Functional link Artificial Recurrent Neural Network (PFLARNN) has been proposed for the prediction of financial time series data. Although different types of polynomial functions have been used for low complexity neural network architectures earlier for stock market prediction, a comparative study is needed to choose the optimal combinations of the nonlinear functions for a reasonably accurate forecast. Further a recurrent version of the Functional link neural network is used to model more accurately a chaotic time series like stock market indices with a lesser number of nonlinear basis functions. The proposed PFLARNN model when trained with the well known gradient descent algorithm produces reasonable accuracy with a choice of range of weight parameters of the network. However, to improve the accuracy of the forecast further, the weight parameters of the recurrent functional neural network are optimized using an evolutionary learning algorithm like the differential evolution (DE). A comparison with other well known neural architectures shows that the proposed low complexity neural model can provide significant prediction accuracy for one day advance and speed of convergence using the International Business Machines Corp. (IBM) stock market indices.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - D.K. Bebarta AU - Birendra Biswal AU - P.K. Dash PY - 2015 DA - 2015/12/01 TI - Polynomial Based Functional Link Artificial Recurrent Neural Network adaptive System for predicting Indian Stocks JO - International Journal of Computational Intelligence Systems SP - 1004 EP - 1016 VL - 8 IS - 6 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2015.1099910 DO - 10.1080/18756891.2015.1099910 ID - Bebarta2015 ER -