Sale forecast for basic commodities based on artificial neural networks prediction

Silva J.; Villa J.V.; Cabrera D. · Advances in Intelligent Systems and Computing (2020) · DOI: 10.1007/978-3-030-23887-2_5

Resumen

The objective of this paper is to carry out the comparison and selection of a method to forecast sales of basic food products efficiently. The source of data comes from a set of popular markets in the main departments of Colombia. The methods and methodologies used are: Hold Method, Winters, the Box Jenkins methodology (ARIMA) and an Artificial Neural Network. The results show that the artificial neural network obtained a better performance achieving the lowest mean square error. © Springer Nature Switzerland AG 2020.

Instituciones: Universidad Peruana de Ciencias Aplicadas