FORECASTING COFFEE PRODUCTION AND EXPORT DEMAND IN COLOMBIA BAJO ARTIFICIAL INTELLIGENCE CONCEPTS

Abstract

Artificial intelligence is having a positive impact on the world thanks to its ability to solve real-life problems. In this context, a research project is being carried out that proposes the use of artificial intelligence techniques to predict the demand for production and export of Colombian coffee. To estimate a future prediction from historical data, two machine learning techniques will be used: support vector machines and the Long Short-Term Memory (LSTM) type recurrent neural network (RNN), both applied for predictions with the regression method.

In this project, data from 107 monthly records between 2014 and 2022 will be used to predict the demand for coffee production and export. 17 variables that affect coffee exports and 12 variables that affect production are identified. These variables are included in a CSV file containing 107 records. During training, 70% of the data will be used for the predictive model to learn, while the remaining 30% will be used to visualize the performance of the predictive model.