Title: On the fusion of regression and neural network methods

Authors: Augustine Pwasong; Saratha Sathasivam

Addresses: School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia ' School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia

Abstract: In this paper, a cascade forward back propagation neural (CFBN) network model and an ordinary least squares (OLS) regression model are fused together to form a hybrid model called 'hybrid OLS-CFBN' technique. The hybrid model was applied on the crude oil data of the Nigerian national petroleum corporation (NNPC) to determine the forecasting performance of the model. The fusion was made by the Bayesian model averaging (BMA) technique, to obtain a combined forecast from the two separate methods, that is, the CFBN and the OLS methods. The fusion produced the hybrid OLS-CFBN model which was used on the difference and log difference series of the NNPC data. The results indicate that the combined forecast have better forecasting performance greater than the standalone methods on the difference series based on the mean square error sense. The analysis for this study was simulated using MATLAB software, version 8.03.

Keywords: BMA; Bayesian model averaging; artificial neural networks; ANNs; MAE; mean absolute error; root mean square error; RMSE; time series forecasting; OLS regression; ordinary least squares; nonlinear; crude oil production; Nigeria; simulation; Nigerian National Petroleum Corporation; NNPC.

DOI: 10.1504/IJISTA.2016.078357

International Journal of Intelligent Systems Technologies and Applications, 2016 Vol.15 No.3, pp.255 - 280

Received: 06 Apr 2015
Accepted: 09 Feb 2016

Published online: 02 Aug 2016 *

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