Aplicación de redes neuronales artificiales para el pronóstico de precios de café

Karen Shirley Cardozo Rueda

doi:10.24054/rcta.v1i39.1403

Authors

Karen Shirley Cardozo Rueda Fundación universitaria de San Gil

DOI:

https://doi.org/10.24054/rcta.v1i39.1403

Keywords:

Artificial neural networks, backpropagation, perceptron, price forecasting

Abstract

This work employs the ability to make forecasts with non-linear models, structured information and supervised models,that artificial neural networks (ANN) make use of, to predict the prices of the arabic-type coffee, serving as support to small and medium coffee entrepreneurs that harvest this product. In addition, the forecasting model is established by the closing value of the dollar and the coffee price. Also, the theoretical development of the basic foundations of neural networks Backpropagation and weight adjustment algorithms. The characterization is performed, followed by a simulation of the forecast process using the Python programming language with the Numpy and Matploit libraries. The result indicates an astonishing amount of success achieved from this model and its performance at learning to minimize errors.

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