Published Mar 16, 2012



PLUMX
Almetrics
 
Dimensions
 

Google Scholar
 
Search GoogleScholar
Downloads


Juan David Velasquez-Henao, PhD

Carlos Jaime Franco-Cardona, PhD

Yris Olaya-Morales, PhD

##plugins.themes.bootstrap3.article.details##

Abstract

Recently, Ghiassi, Saidane and Zimbra [Int J Forecasting, vol. 21, 2005, pp. 341-362] presented a dynamicarchitecture neural network for time series prediction which performs significantly better than traditional artificial neural networks and the ARIMA methodology. The main objective of this article is to prove that the original DAN2 model can be rewritten as an additive model. We show that our formulation has several advantages: First, it reduces the total number of parameters to estimate; second, it allows estimating all the linear parameters by using ordinary least squares or ridge regression; and, finally, it improves the search for the global minimum of the error function used to estimate the model parameters. To assess the effectiveness of our approach, we estimate two models for one of the time series used as a benchmark when the original DAN2 model was proposed. The results indicate that our approach is able to find models with similar or better accuracy than the original DAN2.

Keywords

Neural networks (computer science), artificial intelligence, logic programming, time-series analysisRedes neurales (computadores), inteligencia artificial, programación lógica, análisis de series de tiempo

References
GHIASSI, M. and BURNLEY, C. Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems. Expert Systems with Applications. 2010, vol. 37, pp. 3118-3128.
GHIASSI, M. and NANGOY, S. A dynamic artificial neural network model for forecasting nonlinear processes. Computers & Industrial Engineering. 2009, vol. 57, núm. 1, pp. 287-297.
GHIASSI, M. and SAIDANE, H. A dynamic architecture for artificial neural networks. Neurocomputing. 2005, vol. 63, pp. 397-413.
GHIASSI, M.; SAIDANE, H. and ZIMBRA, D. K. A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting. 2005, vol. 21, núm. 2, pp. 341-362.
GHIASSI, M.; ZIMBRA, D.K. and SAIDANE, H. Medium term system load forecasting with dynamic artificial neural network model. Electric Power Systems Research. 2006, vol. 76, pp. 302-316.
GHIASSI, M.; ZIMBRA, D.K. and SAIDANE, H. Urban water demand forecasting with a dynamic artificial neural network model. Journal of Water Resources Planning and Management. 2008, vol. 134, núm. 2, pp. 138-146.
GOMES, G. S. S.; MAIA, A. L. S.; LUDERMIR, T. B.; CARVALHO, F. and ARAUJO, A. F. R. Hybrid model with dynamic architecture for forecasting time series. Proceedings of the International Joint Conference on Neural Networks. IEEE Computer Society. 2006, pp. 7133-7138.
GURESEN, E.; KAYAKUTLU, G. and DAIM, T.U. Using artificial neural network models in stock market index prediction. Expert Systems with Applications. 2011, vol. 38, pp. 10389-10397.
WANG, J.; NIU, D. and LI, L. Middle-long term load forecasting based on dynamic architecture for artificial neural network. Journal of Information and Computational Science. 2010, vol. 7, núm. 8, pp. 1711-1717.
VELÁSQUEZ, J. D. and FRANCO, C .J. Prediction of the prices of electricity contracts using a neuronal network with dynamic architecture. Innovar. 2010, vol. 20, núm. 36, pp. 7-14.
How to Cite
Velasquez-Henao, J. D., Franco-Cardona, C. J., & Olaya-Morales, Y. (2012). A A review of DAN2 (dynamic architecture for artificial neural networks) model in time series forecasting. Ingenieria Y Universidad, 16(1), 135. https://doi.org/10.11144/Javeriana.iyu16-1.rdmo
Section
Articles

Most read articles by the same author(s)