Abstract
The design of models for time series prediction has found a solid foundation on statistics. Recently, artificial neural networks have been a good choice as approximators to model and forecast time series. Designing a neural network that provides a good approximation is an optimization problem. Given the many parameters to choose from in the design of a neural network, the search space in this design task is enormous. When designing a neural network by hand, scientists can only try a few of them, selecting the best one of the set they tested. In this paper we present a hybrid approach that uses evolutionary computation to produce a complete design of a neural network for modeling and forecasting time series. The resulting models have proven to be better than the ARIMA and the hand-made artificial neural network models.
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Flores, J.J., Loaeza, R., Rodríguez, H., Cadenas, E. (2009). Wind Speed Forecasting Using a Hybrid Neural-Evolutive Approach. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds) MICAI 2009: Advances in Artificial Intelligence. MICAI 2009. Lecture Notes in Computer Science(), vol 5845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05258-3_53
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DOI: https://doi.org/10.1007/978-3-642-05258-3_53
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