Sistema de recomendación para el abastecimiento de remolcadores en embarcaciones de hidrocarburos utilizando técnicas de machine learning

Abstract

CONDITION FOR PUBLICATION OF PROJECT. The forecast for the supply of tugboats at a maritime oil terminal is addressed. The proposed objective was to perform a descriptive and predictive analysis of the demand for tugboats using machine learning techniques. The hypothesis is that ML models can be reliable, as can statistical-econometric and deep learning models; the justification lies in the absence of technological tools in port logistics. To develop this solution, the KDD (Knowledge Discovery in Databases) methodology was used for a heuristic and empirical approach. Operational and meteorological data were integrated and refined; the temporal structure (level, trend, seasonality, and noise) was explored, and statistical and visual tests were applied to the characteristics of the series. Gradient trees were modeled using the Skforecast library in a univariate and multistep approach, under computational constraints. To avoid data leakage, cross-validation was used in conjunction with the backtesting technique. Appropriate metrics were adapted to the series to evaluate performance under real conditions, reinforcing validity in operational contexts with high variability and uncertainty. The results obtained a model that achieved 81.11% coverage and an accuracy of RMdSE=0.051340. In conclusion, this proposal lays the foundation for more informed, efficient, and proactive decision-making in a sector. Keywords: Skforecast; time series forecasting; tugboats; oil ports; forecasting.