Dynamic Correlation Approach to Early Stopping in Neural Forecasting of Macroeconomic Indices

• Autorzy: Michalak K. , Raciborski R.
• Języki publikacji: EN

Abstrakty

EN

Neural networks are commonly used for modelling he behaviour of macroeconomic indices. One of the issues that arise in neural networks applications is that when a neural network is trained for too long the quality of the predictions tends to drop with the increasing number of training iterations. To overcome this problem various methods of early stopping are employed. In this paper an early stopping method based on dynamic correlation between time series introduced in our previous work is validated on macroeconomic indices. In correlation-based approach the decision whether to stop training or not is based on the prediction error value calculated for the time series that has possibly the lowest mean dynamic correlation with the time series being predicted. Experimental results show advantages of dynamic correlation method in predicting the behaviour of industrial production indices for EU-25 member countries. Experiments show that correlation-based method produces lower forecast errors compared to the early stopping performed using a subset of historical samples from the same time series. The results obtained imply that this observation is of a high statistical significance.

Słowa kluczowe

EN

neural network; modelling

PL

sieci neuronowe; modelowanie

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2007
• Tom: Vol. 15, nr 2
• Strony: 27--40
• Opis fizyczny: Bibliogr. 7 poz.

Twórcy

autor

Michalak K.

autor

Raciborski R.

• Wroclaw University of Technology Institute of Applied Informatics,

Bibliografia

• [1] Bishop, Ch. M.: Neural Networks for Pattern Recognition, Oxford University Press, 1995.
• [2] Croux, Ch., Formi, M., Reichlin, L.: A Measure of Comovement for Economic Variables: Theory and Empirics, CEPR Discussion Paper 2339, 1999.
• [3] Gupta, M. M.: Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory, Wiley-IEEE Press, 2003.
• [4] Michalak K., Raciborski R.: Dynamic Correlation Approach to Early Stopping in Artificial Neural Network Training. Macroeconomic Forecasting Example, 5th International Conference on Intelligent Systems Design and Applications, ed. H. Kwaśnicka, M. Paprzycki, IEEE Computer Society Press, 2005.
• [5] Munoz, P. D.: Methodology of Short-term Business Statistics Interpretation and Guidelines, Office for Official Publications of the European Communities, 2002.
• [6] Shapiro, R. J.: 1997 Economic Census. Manufacturing Industry Series, U.S. Census Bureau, 1999.
• [7] Euostat web page: http://europa.eu.int/comm/eurostat