Demand Forecasting for Inventory Management using Limited Data Sets: A Case Study from the Oil Industry

Jorge Ivan Romero-Gelvez, Esteban Felipe Villamizar, Olmer Garcia-Bedoya, Jorge Aurelio Herrera Cuartas
2020 International Conference on Applied Informatics  
This document's main focus is to present a way to solve forecasting issues using open source tools for time series analysis. First, we present an introduction to the hydrocarbon sector and time series analysis, later we focus on the solution methods based on supervised learning trained (support vector regression) with bio-inspired algorithms (Particle swarm optimization). We expose some benefits of use support vector machines and open source tools that focuses on variables like trend and
more » ... lity. In this work, we chose the fb-prophet package and support vector regressor with scikit-learn as the primary tools because they have representative results dealing with limited data sets and Particle swarm optimization as training algorithm because of their speed and adaptability. Finally, we show the results and compare them with their RMSE obtained.
dblp:conf/icai2/Romero-GelvezVG20 fatcat:qs3hipupejc7nc2uq36mi4ug6m