A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
2005 Special Issue: A comparative study of autoregressive neural network hybrids
Neural Networks - 2005 Special issue: IJCNN 2005
Improved supply chain management based on hybrid demand forecasts
Applied Soft Computing
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Generalized dynamical fuzzy model for identification and prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, using ANNs to model linear problems have yielded mixed results, and hence; it is not wise to apply them blindly to any type of data. This is the reason that hybrid methodologies combining linear models such as ARIMA and nonlinear models such as ANNs have been proposed in the literature of time series forecasting. Despite of all advantages of the traditionalmethodologies for combining ARIMA and ANNs, they have some assumptions that will degenerate their performance if the opposite situation occurs. In this paper, a new methodology is proposed in order to combine the ANNs with ARIMA in order to overcome the limitations of traditional hybrid methodologies and yield more general and more accurate hybrid models. Empirical results with Canadian Lynx data set indicate that the proposed methodology can be a more effective way in order to combine linear and nonlinear models together than traditional hybrid methodologies. Therefore, it can be applied as an appropriate alternative methodology for hybridization in time series forecasting field, especially when higher forecasting accuracy is needed.