Predicting the price of Brazilian Natural coffee using statistical machine learning models
Keywords:
Modelling, Coffee Price, Commodities, Brazil, Machine LearningAbstract
The knowledge of price behavior becomes extremely useful in decision-making by coffee producers. However, the conclusions about finding determinants of prices in the agricultural area are ambiguous in the literature, because problems in the methodologies adopted, errors related to selected variables and ignored statistical hypotheses are some of the reasons for divergent results. Given the importance of coffee in the Brazilian economy, the main of this work is to study models known as Statistical Machine Learning for the Brazilian coffee price prediction. As a result, in descending order, the models that obtained the best predictive powers were Support Vector Machine (SVM) with Linear Kernel, followed by LASSO, SVM with Gaussian Kernel, Boosting, Regression Tree, K-NN, and Random Forest models. In addition, most of the models obtained a high correlation between their results and corroborated the choice of the variables
that most affect the price. We further believe that this approach helps to produce more robust conclusions about the determinants of coffee price variability and thus is a potential tool in risk management and control by the administrators.
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