System identification: theory for the user
System identification: theory for the user
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
An ARMA order selection method with fuzzy reasoning
Signal Processing - Special section on information theoretic aspects of digital watermarking
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Building ARMA Models with Genetic Algorithms
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Computers and Operations Research - Special issue: Emerging economics
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
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
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
Optimizing feedforward artificial neural network architecture
Engineering Applications of Artificial Intelligence
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
A neural network ensemble method with jittered training data for time series forecasting
Information Sciences: an International Journal
Forecasting time series using principal component analysis with respect to instrumental variables
Computational Statistics & Data Analysis
A dynamic architecture for artificial neural networks
Neurocomputing
A comparison between neural-network forecasting techniques-case study: river flow forecasting
IEEE Transactions on Neural Networks
A hybrid linear-neural model for time series forecasting
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Using artificial neural network models in stock market index prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Forecasting model selection through out-of-sample rolling horizon weighted errors
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
A new linear & nonlinear artificial neural network model for time series forecasting
Decision Support Systems
Prediction of energy's environmental impact using a three-variable time series model
Expert Systems with Applications: An International Journal
Predicting the helpfulness of online reviews using multilayer perceptron neural networks
Expert Systems with Applications: An International Journal
Unsupervised learning algorithm for time series using bivariate AR(1) model
Expert Systems with Applications: An International Journal
Artificial Neural Network Expert System for Integrated Heart Rate Variability
Wireless Personal Communications: An International Journal
Neural network ensemble operators for time series forecasting
Expert Systems with Applications: An International Journal
International Journal of Applied Evolutionary Computation
Fuzzy artificial neural network p, d, q model for incomplete financial time series forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.06 |
Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. 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 the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The 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 artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.