Sale forecast for basic commodities based on artificial neural networks prediction
Documento de Conferencia
2020
Universidad de la Costa
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.
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Sale forecast for basic commodities based on artificial neural networks prediction.pdf
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Título: Sale forecast for basic commodities based on artificial neural networks prediction.pdf
Tamaño: 171.0Kb
PDFLEER EN FLIP
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