Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Building ARMA Models with Genetic Algorithms
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Genetic Programming for Mining DNA Chip Data from Cancer Patients
Genetic Programming and Evolvable Machines
A neural network ensemble method with jittered training data for time series forecasting
Information Sciences: an International Journal
GP on SPMD parallel graphics hardware for mega Bioinformatics data mining
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (1143 - 1198) " Distributed Bioinspired Algorithms"; Guest editors: F. Fernández de Vega, E. Cantú-Paz
Wind Speed Forecasting Using a Hybrid Neural-Evolutive Approach
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
ARIMA Model Estimated by Particle Swarm Optimization Algorithm for Consumer Price Index Forecasting
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
The curse of dimensionality in data mining and time series prediction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Pruning feedforward neural network search space using local lipschitz constants
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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Evolutionary design of time series predictors is a field that has been explored for several years now. The levels of design vary in the many works reported in the field. We decided to perform a complete design and training of ARIMA models using Evolutionary Computation. This decision leads to high dimensional search spaces, whose size increases exponentially with dimensionality. In order to reduce the size of those search spaces we propose a method that performs a preliminary statistical analysis of the inputs involved in the model design and their impact on quality of results; as a result of the statistical analysis, we eliminate inputs that are irrelevant for the prediction task. The proposed methodology proves to be effective and efficient, given that the results increase in accuracy and the computing time required to produce the predictors decreases.