Forecasting time series with a new architecture for polynomial artificial neural network

Authors:
E. Gómez-Ramírez;K. Najim;E. Ikonen
Affiliations:
Laboratorio de Investigación y Desarrollo de Tecnología Avanzada, Lidetea, Universidad la Salle, Benjamín Franklin No. 47 Col. Condesa, CP 06140, México, DF, México;Process Control Laboratory, E.N.S.I.A.C.E.T., 118, route de Narbonne, 31077 Toulouse Cedex 4, France;Infotech Oulu, Department of Process and Environmental Engineering, Systems Engineering Laboratory, P.O. Box 4300 FIN-90014 University of Oulu, Oulu PR242, Linnanmaa, Finland
Venue:
Applied Soft Computing
Year:
2007

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Abstract

Polynomial artificial neural networks (PANN) have been shown to be powerful for forecasting nonlinear time series. The training time is small compared to the time used by other algorithms of artificial neural networks and the capacity to compute relations between the inputs and outputs represented by every term of the polynomial. In this paper a new structure of polynomial is presented that improves the performance of this type of network considering only non-integers exponents. The architecture adaptation uses genetic algorithm (GA) to find the optimal architecture for every example. Some examples of sunspots and chaotic time series are presented.