Time series: Empirical characterization and artificial neural network-based selection of forecasting techniques

  • Authors:
  • Marí/ Guadalupe Villarreal Marroquí/n;Mary Carmen Acosta Cervantes;José/ Luis Martí/nez Flores;Mauricio Cabrera-Rí/os

  • Affiliations:
  • Graduate Program in Systems Engineering, Universidad Autó/noma de Nuevo Leó/n, San Nicolá/s de los Garza, Nuevo Leó/n 66450, Mé/xico;Department of Industrial Engineering, Universidad Autó/noma de Tamaulipas, Ciudad Victoria, Tamaulipas 87000, Mé/xico;Center of Interdisciplinary Graduate Studies, Universidad Popular Autó/noma del Estado de Puebla, Puebla, Puebla 72160, Mé/xico;(Correspd. Tel.: +787 8324040 Ext. 3240/ Fax: +787 2653820/ E-mail: mauricio.cabrera1@upr.edu) Industrial Engineering Department, University of Puerto Rico at Mayagü/ez, Mayagü/ez, 00681-9 ...

  • Venue:
  • Intelligent Data Analysis
  • Year:
  • 2009

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Abstract

In this work a method to facilitate the elaboration of a forecast for people with little statistical training is proposed. The method uses a rather simple yet sufficiently accurate time series characterization that allowed training a series of artificial neural networks (ANNs) to predict the forecasting performance of several statistical techniques. A case study is presented to demonstrate the application of the method. All techniques used, including the ANN, were conveniently coded in MS Excel so the computational requirements are modest. Furthermore, the results can be tabulated for quick consultation.