Felipe Villamizar - Academia.edu (original) (raw)
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Papers by Felipe Villamizar
instname:Universidad de Bogotá Jorge Tadeo Lozano, 2020
The main focus of this document is to present a way to solve forecasting issues using open source... more The main focus of this document 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 in 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 seasonality (in this work we chose fb-prophet package and support vector regressor with scikit-learn as main tools because they have representative results dealing with limited data sets, and Particle swarm optimization as training algorithm because their speed and adaptability). Finally we show the results and compare them with their RMSE obtained.
instname:Universidad de Bogotá Jorge Tadeo Lozano, 2020
The main focus of this document is to present a way to solve forecasting issues using open source... more The main focus of this document 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 in 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 seasonality (in this work we chose fb-prophet package and support vector regressor with scikit-learn as main tools because they have representative results dealing with limited data sets, and Particle swarm optimization as training algorithm because their speed and adaptability). Finally we show the results and compare them with their RMSE obtained.