Affiliations: [a] Graduate Program in Systems Engineering, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Nuevo León 66450, México | [b] Department of Industrial Engineering, Universidad Autónoma de Tamaulipas, Ciudad Victoria, Tamaulipas 87000, México | [c] Center of Interdisciplinary Graduate Studies, Universidad Popular Autónoma del Estado de Puebla, Puebla, Puebla 72160, México | [d] Industrial Engineering Department, University of Puerto Rico at Mayagüez, Mayagüez, 00681-9000, Puerto Rico
Correspondence:
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Corresponding author: Mauricio Cabrera-Ríos, Industrial Engineering Department, University of Puerto Rico at Mayagüez, PO box 9000, Mayagüez, 00681-9000, Puerto Rico. Tel.: +787 8324040 Ext. 3240; Fax: +787 2653820; E-mail: mauricio.cabrera1@upr.edu.
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.
Keywords: Artificial neural networks, forecasting techniques, time series characterization
