International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 7, No. 9, 8 Aug. 2015
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Forecasting Performance of Random Walk with Drift and Feed Forward Neural Network Models
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Author(s)
Index Terms
Linear, Forecasting, Error, Nonlinear, Neural Network and Drift
Abstract
In this study, linear and nonlinear methods were used to model forecasting performances on the daily crude oil production data of the Nigerian National Petroleum Corporation (NNPC). The linear model considered here is the random walk with drift, while the nonlinear model is the feed forward neural network model. The results indicate that nonlinear methods have better forecasting performance greater than linear methods based on the mean error square sense. The root mean square error (RMSE) and the mean absolute error (MAE) were applied to ascertain the assertion that nonlinear methods have better forecasting performance greater than linear methods. Autocorrelation functions emerging from the increment series, that is, log difference series and difference series of the daily crude oil production data of the NNPC indicates significant autocorrelations. As a result of the foregoing assertion we deduced that the daily crude oil production series of the NNPC is not firmly a random walk process. However, the original daily crude oil production series of the NNPC was considered to be a random walk with drift when we are not trying to forecast immediate values. The analysis for this study was simulated using MATLAB software, version 8.03.
Cite This Paper
Augustine D. Pwasong, Saratha AP. Sathasivam, "Forecasting Performance of Random Walk with Drift and Feed Forward Neural Network Models", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.9, pp.49-56, 2015. DOI:10.5815/ijisa.2015.09.07
Reference
[1]G. E. Box, G. M. Jenkins, Time series analysis: forecasting and control, revised ed, Holden-Day, 1976.
[2]R. P. Lippmann, An introduction to computing with neural nets, ASSP Magazine, IEEE 4 (1987) 4–22.
[3]N. Ibrahim, R. R. Abdullah, M. Saripan, Artificial neural network approach in radar target classification, Journal of Computer Science 5 (2009) 23.
[4]C. G. Carmichael, A study of the accuracy, completeness, and efficiency of artificial neural networks and related inductive learning techniques (2001).
[5]O. N. A. AL-Allaf, Cascade-forward vs. function fitting neural network for improving image quality and learning time in image compression system, in: Proceedings of the World Congress on Engineering, volume 2, pp. 4–6.
[6]S. B. Roy, K. Kayal, J. Sil, Edge preserving image compression technique using adaptive feed forward neural network., in: EuroIMSA, pp. 467–471.
[7]P. A. Idowu, C. Osakwe, A. A. Kayode, E. R. Adagunodo, Prediction of stock market in nigeria using artificial neural network, International Journal of Intelligent Systems and Applications (IJISA) 4 (2012) 68.
[8]V. Bianco, O. Manca, S. Nardini, Electricity consumption forecasting in Italy using linear regression models, Energy 34 (2009) 1413–1421.
[9]M. Baghebo, T. O. Atima, The impact of petroleum on economic growth in Nigeria, Global Business and Economics Research Journal 2 (2013) 102–115.
[10]S. Singh, J. Gill, Temporal weather prediction using back propagation based genetic algorithm technique (2014).
[11]S. K. Nanda, D. P. Tripathy, S. K. Nayak, S. Mohapatra, Prediction of rainfall in India using artificial neural network (ann) models, International Journal of Intelligent Systems and Applications (IJISA) 5 (2013) 1.
[12]D. Furundzic, Application example of neural networks for time series analysis:: Rainfall–runoff modeling, Signal Processing 64 (1998) 383–396.
[13]M. Tawfik, Linearity versus non-linearity in forecasting nile river flows, Advances in Engineering Software 34 (2003) 515–524.
[14]L.-C. Chang, F.-J. Chang, Y.-M. Chiang, A two-step-ahead recurrent neural network for stream-flow forecasting, Hydrological Processes 18 (2004) 81–92.
[15]M. Castellano-M´endez, W. Gonz´alez-Manteiga, M. Febrero-Bande, J. M. Prada-S´anchez, R. Lozano-Calder´on, Modeling of the monthly and daily behaviour of the runoff of the xallas river using box–jenkins and neural networks methods, Journal of Hydrology 296 (2004) 38–58.
[16]R. S. Pindyck, The long-run evolution of energy prices, The Energy Journal (1999) 1–27.
[17]S. Radchenko, The long-run forecasting of energy prices using the model of shifting trend, University of North Carolina at Charlotte, Working Paper (2005).
[18]G. Halkos, I. Kevork, Estimating population means in covariance stationary process (2006).
[19]K. A. Krycha, U. Wagner, Applications of artificial neural networks in management science: a survey, Journal of Retailing and Consumer Services 6 (1999) 185–203.
[20]H. R. Maier, G. C. Dandy, Neural network based modeling of environmental variables: a systematic approach, Mathematical and Computer Modeling 33 (2001) 669–682.
[21]S. Haykin, N. Network, A comprehensive foundation, Neural Networks 2 (2004).
[22]J. A. Suykens, J. P. Vandewalle, B. L. de Moor, Artificial neural networks for modeling and control of non-linear systems, Springer Science & Business Media, 1996.
[23]P. Rowland, Forecasting the usd/cop exchange rate: A random walk with a variable drift, Serie Borradores de Econom´ıa (2003).
[24]S. Sathasivam, N. P. Fen, Developing agent based modeling for doing logic programming in hopfield network, Applied Mathematical Sciences 7 (2013) 23–35.
[25]D. Fadare, Modeling of solar energy potential in nigeria using an artificial neural network model, Applied Energy 86 (2009) 1410–1422.
[26]S. Shan, A Levenberg-Marquardt method for large-scale bound-constrained nonlinear least-squares, Ph.D. thesis, The University of British Columbia (Vancouver), 2008.
[27]A. B. D. Becker, Decomposition methods for large scale stochastic and robust optimization problems, Ph.D. thesis, Massachusetts Institute of Technology, 2011.