A Comparative Study on Business Forecasting Accuracy among Neural Networks and Time Series

Dr. Osman Mohamed Abbas


This study shows that neural networks have been advocated as an alternative to traditional statistical forecasting methods. Numerous articles comparing performances of statistical and Neural Networks (NNs) models are available in the literature. The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modeling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts.


forecasting; neural networks; time series.

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Adya, M. and Collopy, F. (1998) How effective are neural networks at forecasting and prediction? A review and evaluation. Journal of Forecasting 17: 481-495.

Anderson, O. (1951) Konjunkturtest und Statistik. Allgemeines Statistical Archives 35: 209-220.

Biart, M. and Praet, P. (1987) The contribution of opinion surveys in forecasting aggregate demand in the four main EC countries. Journal of Economic Psychology 8. 409-428.

Bishop, C.M. (1995) Neural networks for pattern recognition. Oxford University Press, Oxford.

Box, G. and Cox, D. (1964) An analysis of transformation. Journal of the Royal Statistical Society, Series B: 211-64.

Box, G.E.P. and Jenkins, G.M. (1970) Time series analysis: Forecasting and control. Holden Day, San Francisco.

CESifo World Economic Survey (2011), Volume 10, No. 2, May 2011.

Clar, M., Duque, J.C. and Moreno, R. (2007) Forecasting business and consumer surveys indicators – a time-series models competition. Applied Economics 39: 2565-2580.

Claveria, O. (2010) Qualitative survey data on expectations. Is there an alternative to the balance statistic?. In A. T. Molnar (ed.) Economic Forecasting (pp. 181-190). Nova Science Publishers, Hauppauge NY.

Claveria, O., Pons, E. and Suriñach, J. (2006) Quantification of expectations. Are they useful for forecasting?. Economic Issues 11: 19-38.

Clements, M.P. and Smith, J. (1999) A Monte Carlo study of the forecasting performance of empirical SETAR models. Journal of Applied Econometrics 14: 123-141.

Cybenko G. (1989) Approximation by superpositions of a sigmoidal function. Mathematical Control, Signal and Systems 2: 303-314.

Diebold, F.X. and Mariano, R. (1995) Comparing predictive accuracy. Journal of Business and Economic Statistics 13, 253-263.

Diebold, F.X. and Rudebusch, G.D. (1989) Scoring the leading indicators. Journal of Business 62: 369-391.

Funahashi, K. (1989) On the approximate realization of continuous mappings by neural networks. Neural Networks 2: 183-192.

Ghonghadze, J. and Lux, T. (2009) Modelling the dynamics of EU economic sentiment indicators: An inter-action based approach. Kiel Working Paper, 1487. [17] Institut für Weltwirtschaft, Kiel.

Hansen, B. (1997) Inference in TAR models. Studies in Nonlinear Dynamics and Econometrics 2: 1-14.

Hendry, D.F. and Clements, M.P. (2003) Economic forecasting: some lessons from recent research. Economic Modellig 20: 301-329.

Hill, T., Marquez, L., O’Connor, M. and Remus, W. (1994) Artificial neural network models for forecasting and decision making. International Journal of Forecasting 10: 5-15.

Hornik, K., Stinchcombe, M. and White, H. (1989) Multilayer feedforward networks are universal approximations. Neural Networks 2: 359-366.

Kaastra, I. and Boyd, M. (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10: 215-236.

Kock, A. B. and Teräsvirta, T. (2011) Forecasting with nonlinear time series models in M.P. Clements and D.F. Hendry (eds.) Oxford Handbook of Economic Forecasting (pp. 61-87). Oxford University Press, Oxford.

Kuan C. and White, H. (1994) Artificial neural networks: an econometric perspective. Econometric Reviews 13: 1-91.

Masters, T. (1993) Practical neural networks recipes in C++. Academic Press, London.

Nakamura E. (2005) Inflation forecasting using a neural network. Economics Letters 86: 373-378.

Palmer, A., Montaño, J.J. and Sesé, A. (2006) Designing an artificial neural network for forecasting tourism time-series. Tourism Management 27: 781-790.

Parigi, G. and Schlitzer, G. (1995) Quarterly forecasts of the Italian business-cycle by means of monthly economic indicators. Journal of Forecasting 14: 117-141.

Qi, M. (2001) Predicting US recessions with leading indicators via neural network models. International Journal of Forecasting 17: 383-401.

Ripley, B. D. (1996) Pattern recognition and neural networks. Cambridge University Press,Cambridge.

Song, H. and Li, G. (2008) Tourism demand modelling and forecasting – a review of recent research. Tourism Management 29: 203-220.

Stangl, A. (2008) Essays on the measurement of economic expectations. Dissertation. Universität München, Munich.

Stock, J. H. and Watson, M.W. (2003) Forecasting output and inflation: the role of asset prices. Journal of Economic Literature 41: 788-829.

Swanson, N. R. and White, H. (1997) Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models. International Journal of Forecasting 13: 439-461.

Wasserman, P. D. (1989) Neural computing: Theory and practice. Van Nostrand Reinhold, New York.

Zhang, G., Patuwo, B.E. and Hu, M.Y. (1998) Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting 14: 35-62.


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