Colombian Coffee Price Forecast via LSTM Neural Networks

Yoe A. Herrera-Jaramillo, Johana C. Ortega-Giraldo, Alejandro Acevedo-Amorocho, Duwamg Prada-Marin

    Producción científica: Capítulo del libro/informe/acta de congresoCapítulo de libro resultado de investigaciónrevisión exhaustiva


    This work deals with the contributions Machine Learning techniques can bring into the coffee growing conglomerate, committees and other points in the production and marketing chain involved in the dynamics of this commodity. It is well known that the different variables that interact with prices both nationally and internationally have a direct, dramatic affect on the sector under study. In this work, we summarize an extensive review of the coffee price dynamics and the forecast techniques used in this eld. In addition, the internal coffee price in Colombia has been modeled using a long short-term memory (LSTM) recurrent neural network that was chosen as the one of better performance out of three original models. The archetype that evidenced a pertinent superiority of fitness within the parameters specified for this type of model is composed of a linear self-regressive component, plus a multi-layer perceptron-type artificial neural network with twenty (40) LSTM cells neurons in the hidden layer. This epitome captures the chaotic coffee price dynamics. The normalized residuals of the model are uncorrelated and homoscedastic and follow a normal distribution. The results indicate that the current price depends on the prices that occurred in the last four (4) years. This tool can be used to help the coffee growing community to better design alternatives to overcome difficulties with the price of the grain, and this makes it a Logistics solution for them.

    Idioma originalInglés
    Título de la publicación alojadaLecture Notes in Intelligent Transportation and Infrastructure
    EditorialSpringer Nature
    Número de páginas17
    EstadoPublicada - 2021

    Serie de la publicación

    NombreLecture Notes in Intelligent Transportation and Infrastructure
    VolumenPart F1390
    ISSN (versión impresa)2523-3440
    ISSN (versión digital)2523-3459

    Nota bibliográfica

    Publisher Copyright:
    © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.


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