Time series forecasting using cascade correlation networks

Artificial neural networks, especially multilayer perceptrons, have been recognised as being a powerful technique for forecasting nonlinear time series; however, cascade-correlation architecture is a strong competitor in this task due to it incorporating several advantages related to the statistical identification of multilayer perceptrons. This paper compares the accuracy of a cascade-correlation neural network to the linear approach, multilayer perceptrons and dynamic architecture for artificial neural networks (DAN2) to determine whether the cascade-correlation network was able to forecast the time series being studied with more accuracy. It was concluded that cascade-correlation was able to forecast time series with more accuracy than other approaches.

[1]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[2]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[3]  Saeed Heravi,et al.  Linear versus neural network forecasts for European industrial production series , 2004 .

[4]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[5]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[6]  David Zimbra,et al.  A dynamic artificial neural network model for forecasting time series events , 2005 .

[7]  Juan David Velásquez Henao,et al.  Una Comparación entre Estrategias Evolutivas y RPROP para la Estimación de Redes Neuronales , 2007, Rev. Avances en Sistemas Informática.

[8]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[9]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[10]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[11]  Chris Chatfield,et al.  Time series forecasting with neural networks: a comparative study using the air line data , 2008 .

[12]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[13]  T. Rao,et al.  An Introduction to Bispectral Analysis and Bilinear Time Series Models , 1984 .

[14]  Manoochehr Ghiassi,et al.  A dynamic architecture for artificial neural networks , 2005, Neurocomputing.

[15]  Georges A. Darbellay,et al.  Forecasting the short-term demand for electricity: Do neural networks stand a better chance? , 2000 .

[16]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[17]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[18]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[19]  A. M. Walker,et al.  A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the , 1977 .