Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Optimal linear combinations of neural networks
Neural Networks
Combining predictors: comparison of five meta machine learning methods
Information Sciences: an International Journal
A simulation study of artificial neural networks for nonlinear time-series forecasting
Computers and Operations Research
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Neural Networks Approach to the Random Walk Dilemma of Financial Time Series
Applied Intelligence
Regression neural network for error correction in foreign exchange forecasting and trading
Computers and Operations Research
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
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
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
A GMM procedure for combining volatility forecasts
Computational Statistics & Data Analysis
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
Stock and bond return predictability: the discrimination power of model selection criteria
Computational Statistics & Data Analysis
Combining artificial neural network and particle swarm system for time series forecasting
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Forecasting volatility under fractality, regime-switching, long memory and student-t innovations
Computational Statistics & Data Analysis
Time series forecasting using a perturbative intelligent system
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Combined forecasters have been in the vanguard of stochastic time series modeling. In this way it has been usual to suppose that each single model generates a residual or prediction error like a white noise. However, mostly because of disturbances not captured by each model, it is yet possible that such supposition is violated. The present paper introduces a two-step method for correcting and combining forecasting models. Firstly, the stochastic process underlying the bias of each predictive model is built according to a recursive ARIMA algorithm in order to achieve a white noise behavior. At each iteration of the algorithm the best ARIMA adjustment is determined according to a given information criterion (e.g. Akaike). Then, in the light of the corrected predictions, it is considered a maximum likelihood combined estimator. Applications involving single ARIMA and artificial neural networks models for Dow Jones Industrial Average Index, S&P500 Index, Google Stock Value, and Nasdaq Index series illustrate the usefulness of the proposed framework.