Document Type : Research Paper

**Authors**

Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran

**Abstract**

Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid models for time series forecasting. Several researches in the literature have been shown that these models can outperform single models. In this paper, the predictive capabilities of three different models in which the autoregressive integrated moving average (ARIMA) as linear model is combined to the multilayer perceptron (MLP) as nonlinear model, are compared together for time series forecasting. These models are including the Zhang’s hybrid ANNs/ARIMA, artificial neural network (p,d,q), and generalized hybrid ANNs/ARIMA models. The empirical results with three well-known real data sets indicate that all of these methodologies can be effective ways to improve forecasting accuracy achieved by either of components used separately. However, the generalized hybrid ANNs/ARIMA model is more accurate and performs significantly better than other aforementioned models.

**Keywords**

- Artificial neural networks (ANNs)
- Auto-Regressive Integrated Moving Average (ARIMA)
- Time series forecasting
- Hybrid linear/nonlinear models

**Main Subjects**

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[5] Berardi V.L., Zhang G.P. (2003), An empirical investigation of bias and variance in time series forecasting: modeling considerations and error evaluation; IEEE Transactions on Neural Networks 14(3); 668–679.

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[36] Khashei M., Bijari M. (2011), A novel hybridization of artificial neural networks and ARIMA models for time series forecasting; Applied Soft Computing 11; 2664–2675.

[37] Khashei M., Bijari M., Raissi GH.A. (2009), Improvement of Auto-Regressive Integrated Moving Average Models Using Fuzzy Logic and Artificial Neural Networks (ANNs); Neurocomputing 72; 956– 967.

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[39] Kim H., Shin K. (2007), A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets; Applied Soft Computing 7; 569–576.

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[41] Ljung L. (1987), System Identification Theory for the User; Prentice-Hall, Englewood Cliffs, NJ.

[42] Luxhoj J.T., Riis J.O., Stensballe B. (1996), A hybrid econometric-neural network modeling approach for sales forecasting; Int. J. Prod. Econ. 43; 175–192.

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[44] Meese R.A., Rogoff K. (1983), Empirical exchange rate models of the seventies: do they fit out of samples?; J. Int. Econ. 14; 3–24.

[45] Minerva T., Poli I. (2001), Building ARMA models with genetic algorithms; Lecture Notes in Computer Science 2037; 335–342.

[46] Mizrach B. (1992), Multivariate nearest-neighbor forecasts of EMS exchange rates’; Journal of Applied Econometrics 7; 151–164.

[47] Ong C.-S., Huang J.-J., Tzeng G.-H. (2005), Model identification of ARIMA family using genetic algorithms; Appl. Math. Comput. 164(3); 885–912.

[48] Pai P.F., Lin C.S. (2005), A hybrid ARIMA and support vector machines model in stock price forecasting; Omega 33; 497–505.

[49] Panda C., Narasimhan V. (2007), Forecasting exchange rate better with artificial neural network; Journal of Policy Modeling 29; 227–236.

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[51] Poli I., Jones R.D. (1994), A neural net model for prediction; Journal of American Statistical Association 89; 117–121.

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[76] Zhang G.P. (2003), Time series forecasting using a hybrid ARIMA and neural network model; Neurocomputing 50; 159–175.

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[57] Subba Rao T., Sabr M.M. (1984), An Introduction to Bispectral Analysis and Bilinear Time Series Models; Lecture Notes in Statistics 24; Springer-Verlag, New York.

[58] Tang Y., Ghosal S. (2007), A consistent nonparametric Bayesian procedure for estimating autoregressive conditional densities; Computational Statistics & Data Analysis 51; 4424–4437.

[59] Tang Z., Almeida C., Fishwick P.A. (1991), Time series forecasting using neural networks vs. Box-Jenkins methodology; Simulation 57(5); 303–310.

[60] Tang Z., Fishwick P.A. (1993), Feedforward neural nets as models for time series forecasting; ORSA Journal on Computing 5(4); 374–385.

[61] Taskaya T., Ahmad K. (2005), Are ARIMA neural network hybrids better than single models?; Proceedings of International Joint Conference on Neural Networks (IJCNN 2005); July 31–August 4, Canada.

[62] Taskaya T., Casey M. C. (2005), A comparative study of autoregressive neural network hybrids; Neural Networks 18; 781–789.

[63] Timmermann A., Granger C.W.J. (2004), Efficient market hypothesis and forecasting; Int. J. Forecasting 20; 15–27.

[64] Tong H., Lim K.S. (1980), Threshold autoregressive, limit cycles and cyclical data; Journal of the Royal Statistical Society Series B 42(3); 245–292.

[65] Tsaih R., Hsu Y., Lai C.C. (1998), Forecasting S&P 500 stock index futures with a hybrid AI system; Decision Support Systems 23; 161–174.

[66] Tseng F.M., Yu H.C., Tzeng G.H. (2002), Combining neural network model with seasonal time series ARIMA model; Technological Forecasting & Social Change 69; 71–87.

[67] Voort M.V.D., Dougherty M., Watson S. (1996), Combining Kohonen maps with ARIMA time series models to forecast traffic flow; Transportation Research Part C: Emerging Technologies 4; 307–318.

[68] Wedding D.K., Cios K.J. (1996), Time series forecasting by combining RBF networks, certainty factors, and the Box–Jenkins model; Neurocomputing 10; 149–168.

[69] Weigend A.S., Gershenfeld N.A. (1993), Time Series Prediction: Forecasting the Future and Understanding the Past; Addison-Wesley, Reading, MA.

[70] Wold H. (1938), A Study in the Analysis of Stationary Time Series; Almgrist & Wiksell, Stockholm.

[71] Wong C.S., Li W.K. (2000), On a mixture autoregressive model; J. Roy. Statist. Soc. Ser. B 62(1); 91–115.

[72] Yu L., Wang S., Lai K.K. (2005), A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates; Computers and Operations Research 32; 2523–2541.

[73] Yule G.U. (1926), Why do we sometimes get nonsense-correlations between time series? A study in sampling and the nature of time series; J. R. Statist. Soc. 89; 1–64.

[74] Zhang G., Patuwo B.E., Hu M.Y. (1998), Forecasting with artificial neural networks: The state of the art; International Journal of Forecasting 14; 35– 62.

[75] Zhang G.P. (2007), A neural network ensemble method with jittered training data for time series forecasting; Information Sciences 177; 5329–5346.

[76] Zhang G.P. (2003), Time series forecasting using a hybrid ARIMA and neural network model; Neurocomputing 50; 159–175.

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February 2011

Pages 265-285

**Receive Date:**05 March 2010**Revise Date:**20 June 2010**Accept Date:**07 August 2010