Classifier systems and genetic algorithms
Artificial Intelligence
A resource-allocating network for function interpolation
Neural Computation
An introduction to genetic algorithms
An introduction to genetic algorithms
Self-Organizing Maps and Learning Vector Quantization forFeature Sequences
Neural Processing Letters
Multi-step Learning Rule for Recurrent Neural Models: An Application to Time Series Forecasting
Neural Processing Letters
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Evolutionary rule-based system for IPO underpricing prediction
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Fast learning in networks of locally-tuned processing units
Neural Computation
Local Feature Weighting in Nearest Prototype Classification
IEEE Transactions on Neural Networks
Using a case-based reasoning approach for trading in sports betting markets
Applied Intelligence
Block-matching algorithm based on harmony search optimization for motion estimation
Applied Intelligence
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Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able to find predictable local behaviors, and thus, attaining a better level of total prediction. This new method is based on a division of the input space into Voronoi regions by means of Evolution Strategies. Our method has been tested using different time series domains. One of them that represents the water demand in a water tank, through a long period of time. The other two domains are well known examples of chaotic time series (Mackey-Glass) and natural phenomenon time series (Sunspot). Results prove that, in most of cases, the proposed algorithm obtain better results than other algorithms commonly used.