Multilayer feedforward networks are universal approximators
Neural Networks
Neural network models in simulation: a comparison with traditional modeling approaches
WSC '89 Proceedings of the 21st conference on Winter simulation
Generalization and parameter estimation in feedforward nets: some experiments
Advances in neural information processing systems 2
An ARMA order selection method with fuzzy reasoning
Signal Processing - Special section on information theoretic aspects of digital watermarking
Building ARMA Models with Genetic Algorithms
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
2005 Special Issue: A comparative study of autoregressive neural network hybrids
Neural Networks - 2005 Special issue: IJCNN 2005
Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
Forecasting nonlinear time series with neural network sieve bootstrap
Computational Statistics & Data Analysis
A consistent nonparametric Bayesian procedure for estimating autoregressive conditional densities
Computational Statistics & Data Analysis
Forecasting time series using principal component analysis with respect to instrumental variables
Computational Statistics & Data Analysis
A dynamic architecture for artificial neural networks
Neurocomputing
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
A new hybrid methodology for nonlinear time series forecasting
Modelling and Simulation in Engineering
A fuzzy intelligent approach to the classification problem in gene expression data analysis
Knowledge-Based Systems
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
Computers and Industrial Engineering
New robust forecasting models for exchange rates prediction
Expert Systems with Applications: An International Journal
Hybridization of the probabilistic neural networks with feed-forward neural networks for forecasting
Engineering Applications of Artificial Intelligence
Enhanced fuzzy-filtered neural networks for material fatigue prognosis
Applied Soft Computing
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Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing decision makers in many areas. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in combination are quite different. 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 ANNs blindly to any type of data. Autoregressive integrated moving average (ARIMA) models are one of the most popular linear models in time series forecasting, which have been widely applied in order to construct more accurate hybrid models during the past decade. Although, hybrid techniques, which decompose a time series into its linear and nonlinear components, have recently been shown to be successful for single models, these models have some disadvantages. In this paper, a novel hybridization of artificial neural networks and ARIMA model is proposed in order to overcome mentioned limitation of ANNs and yield more general and more accurate forecasting model than traditional hybrid ARIMA-ANNs models. In our proposed model, the unique advantages of ARIMA models in linear modeling are used in order to identify and magnify the existing linear structure in data, and then a neural network is used in order to determine a model to capture the underlying data generating process and predict, using preprocessed data. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid models and also either of the components models used separately.