Polynomial Based Functional Link Artificial Recurrent Neural Network adaptive System for predicting Indian Stocks

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 predi...

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Bibliographic Details
Published in:International journal of computational intelligence systems Vol. 8; no. 6; pp. 1004 - 1016
Main Authors: Bebarta, D. K., Biswal, Birendra, Dash, P. K.
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.12.2015
Springer Nature B.V
Springer
Subjects:
Accuracy
Adaptive systems
backpropagation learning algorithm
Basis functions
Chaos theory
Comparative studies
Complexity
Computer architecture
differential evolution
Evolutionary algorithms
Evolutionary computation
IBM stock indices
Machine learning
MAPE
Mathematical models
Model accuracy
Neural networks
Parameters
PFLARNN
Polynomial functions
Polynomials
Recurrent neural networks
Research Article
Securities markets
Time series
Weight
differential evolution
Polynomial functions
backpropagation learning algorithm
PFLARNN
IBM stock indices
MAPE
AMAPE
ISSN:1875-6891, 1875-6883, 1875-6883
Online Access:Get full text
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Summary: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.
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ISSN:1875-6891
1875-6883
1875-6883
DOI:10.1080/18756891.2015.1099910