System identification: theory for the user
System identification: theory for the user
Neural Networks
Generalization and parameter estimation in feedforward nets: some experiments
Advances in neural information processing systems 2
Fuzzy ARIMA model for forecasting the foreign exchange market
Fuzzy Sets and Systems
An ARMA order selection method with fuzzy reasoning
Signal Processing - Special section on information theoretic aspects of digital watermarking
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
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
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
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
2005 Special Issue: A comparative study of autoregressive neural network hybrids
Neural Networks - 2005 Special issue: IJCNN 2005
A hybrid model for exchange rate prediction
Decision Support Systems
Optimizing feedforward artificial neural network architecture
Engineering Applications of Artificial Intelligence
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
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
A dynamic architecture for artificial neural networks
Neurocomputing
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
New robust forecasting models for exchange rates prediction
Expert Systems with Applications: An International Journal
A new linear & nonlinear artificial neural network model for time series forecasting
Decision Support Systems
Expert Systems with Applications: An International Journal
Implementing support vector regression with differential evolution to forecast motherboard shipments
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Applying quantitative models for forecasting and assisting investment decision making has become more indispensable in business practices than ever before. 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 the literature, several hybrid techniques have been proposed by combining different time series models together, in order to overcome the deficiencies of single models and yield hybrid models that are more accurate. In this paper, in contrast of the traditional hybrid models, a new methodology is proposed in order to construct a new class of hybrid models using a time series model as basis model and a classifier. As classifiers cannot be lonely applied as forecasting model for continuous problems, in the first stage of the proposed model, a forecasting model is used as basis model. Then, the estimated values of the basis model are modified in the second stage, based on the distinguished trend of the residuals of the basis model and the optimum step length, which are respectively calculated by a classifier model and a mathematical programming model. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way in order to construct a more accurate hybrid model than its basis time series model. Therefore, it can be used as an appropriate alternative model for forecasting tasks, especially when higher forecasting accuracy is needed.