Abstract
In this study, a novel combined forecasting model integrating generalized linear auto-regression (GLAR) with artificial neural networks (ANN) was proposed to obtain accurate prediction results and ameliorate forecasting performances. We compare the performance of the new model with the two individual forecasting models-GLAR and ANN. Empirical results obtained reveal that the prediction using the combined model is generally better than those obtained using the individual model of GLAR and ANN in terms of the same evaluation measurements. Our findings reveal that the combined model proposed here can be used as an alternative forecasting tool for coal demand to achieve greater forecasting accuracy and improve prediction quality further.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ning, Y.C.: A Superposition Wavelet-Neural Network Model of Coal Demand Forecast. Journal of China Coal Society 28(1), 108–112 (2003)
Wang, L.J., Sun, J.H.: Coal Required Quantity Forecasting Based on Grey System Theory. Journal of China Coal Society 27(3), 333–336 (2002)
Peng, J.L.: Study on Prediction of the Coal Consumption Based on the Method of Artificial Neural Network. Journal of China University of Mining & Technology 26(3), 63–67 (1997)
Shephard, N.: Generalized Linear Auto Regressions. Economics Working Paper 8, Nuffield College, Oxford (1995)
Firth, D.: Generalized Linear Models. In: Hinkley, D.V., Reid, N., Snell, E.J. (eds.) Statistical Theory and Modeling, pp. 55–82. Chapman & Hall, London (1990)
McCullagh, P., Nelder, J.A.: Generalized Linear Models, 2nd edn. Chapman & Hall, London (1989)
Hill, T., O’Connor, M., Remus, W.: Neural Network Models for Time Series Forecasts, Manage. Sci. 42(7), 1082–1092 (1996)
Chiang, W.C., Urban, T.L., Baldridge, G.W.: A Neural Network Approach to Mutual Fund Net Asset Value Forecasting, Omega. Int. J. Manage. Sci. 24(2), 205–215 (1996)
Liu, M.C., Kuo, W., Sastri, T.: An Exploratory Study of a Neural Network Approach for Reliability Data Analysis. Qual. Reliab. Eng. Int. 11, 107–112 (1995)
Su, C.T., Tong, L.I., Leou, C.M.: Combination of Time Series and Neural Network for Reliability Forecasting Modeling. J. Chin. Inst. Ind. Eng. 14(4), 419–429 (1997)
Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
Denton, J.W.: How Good are Neural Networks for Causal Forecasting? Journal of Business Forecasting 14, 17–20 (1995)
Markham, I.S., Rakes, T.R.: The Effect of Sample Size and Variability of Data on the Comparative Performance of Artificial Neural Networks and Regression. Computers & Operations Research 25, 251–263 (1998)
Chen, A.S., Leung, M.T.: Regression Neural Network for Error Correction in Foreign Exchange Rate Forecasting and Trading. Computers & Operations Research 31(7), 1049–1068 (2004)
Yokum, J.T., Armstrong, J.S.: Beyond Accuracy: Comparison of Criteria Used to Select Forecasting Methods. Int. J. Forecast 11(4), 591–597 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yao, P. (2009). Integrating Generalized Linear Auto-Regression and Artificial Neural Networks for Coal Demand Forecasting. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_112
Download citation
DOI: https://doi.org/10.1007/978-3-642-01507-6_112
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01506-9
Online ISBN: 978-3-642-01507-6
eBook Packages: Computer ScienceComputer Science (R0)