Publication:
Design of artificial neural networks based on genetic algorithms to forecast time series

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URI: http://hdl.handle.net/10016/9783
ISSN: 1615-3871 (impreso)
ISSN: 1860-0794 (online)
ISBN: 978-3-540-74971-4
DOI: 10.1007/978-3-540-74972-1_31
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desgin_gutierrez_ASC_2007_ps.pdf (207.26 KB)
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2007
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Springer
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In this work an initial approach to design Artificial Neural Networks to forecast time series is tackle, and the automatic process to design is carried out by a Genetic Algorithm. A key issue for these kinds of approaches is what information is included in the chromosome that represents an Artificial Neural Network. There are two principal ideas about this question: first, the chromosome contains information about parameters of the topology, architecture, learning parameters, etc. of the Artificial Neural Network, i.e. Direct Encoding Scheme; second, the chromosome contains the necessary information so that a constructive method gives rise to an Artificial Neural Network topology (or architecture), i.e. Indirect Encoding Scheme. The results for a Direct Encoding Scheme (in order to compare with Indirect Encoding Schemes developed in future works) to design Artificial Neural Networks for NN3 Forecasting Time Series Competition are shown.
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Time series forecasting, Artificial neural networks desing, Genetic algorithms, Direct encoding scheme
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Innovations in hybrid intelligent systems, 2007, p. 231-238
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