Statistical Characterization and Optimization of Artificial Neural Networks in Time Series Forecasting: The One–Period Forecast Case

Caracterización Estadística y Optimización de Redes Neuronales Artificiales para Pronóstico de Series de Tiempo: Pronóstico de un Solo Período

María Angélica Salazar Aguilar¹, Guillermo J. Moreno Rodríguez² and Mauricio Cabrera–Ríos^*

¹ División de Posgrado en Ingeniería de Sistemas, Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León, Monterrey, Nuevo León, México e–mail angy@yalma.fime.uanl.mx

² Ingeniería de Tráfico y Optimización de Capacidad, Avantel, Monterrey, Nuevo León, México e–mail gmoreno@avantel.com.mx

^* Corresponding Author
Ph: +52(81) 1492 0637
mcabrera@mail.uanl.mx

Article received on Augost 09, 2005
Accepted on September 08, 2006

Abstract

Time series forecasting is an active area for the application of Artificial Neural Networks (ANNs). Although the selection of an ANN has been greatly simplified, it remains a challenge to adequately determine the ANN's parameters. In this work a method based on statistical analysis and optimization techniques is proposed to select the ANN's parameters for application in time series forecasting. The results on the successful application of the method in a real demand forecasting problem for the telecommunications industry are also reported.

Keywords: Artificial Neural Networks, Time Series Forecasting, Design and Analysis of Experiments.

Resumen

Los pronósticos de series de tiempo constituyen un área activa para la aplicación de Redes Neuronales Artificiales (RNAs). Aunque la selección de una RNA para tal aplicación se ha simplificado grandemente, la falta de un método establecido para asignar los parámetros de las RNAs de una manera adecuada sigue siendo un reto. En este trabajo se propone una metodología basada en técnicas estadísticas y optimización para la selección de parámetros de una RNA para el pronóstico de series de tiempo. La metodología propuesta se demuestra por medio de su aplicación en un problema real de pronóstico de demanda en la industria de las telecomunicaciones.

Palabras Clave: Redes Neuronales Artificiales, Series de tiempo, Análisis y Diseño de Experimentos, Pronósticos.

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Acknowledgements

The authors are grateful to the CONACYT for the scholarship granted to Ms. Salazar for her graduate studies.

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