Predicción del precio de la posición de las exportaciones de flores en Colombia usando modelos de aprendizaje de máquina

Abstract

Exports in emerging economies, such as Colombia, play a vital role in various processes that drive economic dynamics. For instance, 2020 was recorded as a year of recession, where the global economy contracted by 4.4 %. Advanced economies experienced a decline of 5.8 %, while emerging economies in Latin America faced a sharper contraction of 8.1 %. In Colombia, the GDP fell by 7 % due to the pandemic, representing a 3.8 % decrease compared to the GDP of 2019. The restrictions imposed during the pandemic had a significant impact on the flower export market, which, according to Ministerio de Agricultura y Desarrollo Rural de Colombia (2024), contributes to the national economy by generating 130,000 rural jobs. Considering these fluctuations in international markets, it is essential to develop predictive statistical models to understand how key markets for Colombia will behave. Therefore, this research aims to develop predictive models using machine learning techniques, such as Neural Networks LSTM, Random Forests, and XGBoost, as well as traditional statistical models like time series analysis, to forecast export prices by tariff position in Colombia’s flower market.