The Hybrid Forecasting Method SVR-ESAR for Covid-19

  • Juan Frausto Solis Instituto Tecnol´ógico de Ciudad Madero
  • Jose Enrique Olvera Vazquez Tecnológico Nacional de Mexico/1IT Cd Madero
  • Juan Javier González Barbosa Tecnológico Nacional de Mexico/1IT Cd Madero https://orcid.org/
  • Guadalupe Castilla Valdez Tecnológico Nacional de Mexico/1IT Cd Madero
  • Juan Paulo Sánchez Hernández Universidad Politécnica del Estado de Morelos
  • Joaquín Perez-Ortega Tecnológico Nacional de Mexico/CENIDET
  • Ocotlan Diaz Parra Universidad Politécnica de Pachuca
Keywords: SVR forecasting, Covid prediction, hybrid SVR-ARIMA, SVR-Exponential Smoothing

Abstract

We know that SARS-Cov2 produces the new COVID-19 disease, which is one of the most dangerous pandemics of modern times. This pandemic has critical health and economic consequences, and even the health services of the large, powerful nations may be saturated. Thus, forecasting the number of infected persons in any country is essential for controlling the situation. In the literature, different forecasting methods have been published, attempting to solve the problem. However, a simple and accurate forecasting method is required for its implementation in any part of the world. This paper presents a precise and straightforward forecasting method named SVR-ESAR  (Support Vector regression hybridized with the classical Exponential smoothing and ARIMA). We applied this method to the infected time series in four scenarios, which we have taken for the Github repository: the Whole World, China, the US, and Mexico. We compared our results with those of the literature showing the proposed method has the best accuracy.

Author Biographies

Jose Enrique Olvera Vazquez, Tecnológico Nacional de Mexico/1IT Cd Madero

Enrique Olvera is junior research in Computer Science and Machine Learning methods. He main activity research is to perform forecasting models for COVID and another emergent process.

Juan Javier González Barbosa, Tecnológico Nacional de Mexico/1IT Cd Madero

Professor Javier Gonzalez Barbosa is a researcher in Computer Science and  Data Mining. His main research areas are Optimization models, Scheduling, Natural Language, and Forecasting.

Guadalupe Castilla Valdez, Tecnológico Nacional de Mexico/1IT Cd Madero

Dra Guadalupe Castilla Valdez is a professor of Tecnológico Nacional de Méxito @ IT Cd Madero. His main research areas are Heurists, Muliobjective optimization, JSSP, and forecasting.

Juan Paulo Sánchez Hernández, Universidad Politécnica del Estado de Morelos

Professor Juan Paulo is a researcher in Universidad Politécnica del Estado de Morelos, and its principal areas are optimization, computer vision, and machine learning.

Joaquín Perez-Ortega, Tecnológico Nacional de Mexico/CENIDET

Professor Perez Ortega is a researcher in Computer Science and Machine learning with applications in Optimization, Computers Diagnosis, forecasting, and NP-Hard Problems.

Ocotlan Diaz Parra, Universidad Politécnica de Pachuca

Professor Ocotlan Diaz Parra is a researcher in Computer Science-Machine Learning, with applications to several relevant industrial and academic areas.

Published
2020-12-14
How to Cite
Frausto Solis, J., Olvera Vazquez, J. E., González Barbosa, J. J., Castilla Valdez, G., Sánchez Hernández, J. P., Perez-Ortega, J., & Diaz Parra, O. (2020). The Hybrid Forecasting Method SVR-ESAR for Covid-19. International Journal of Combinatorial Optimization Problems and Informatics, 12(1), 42-48. Retrieved from https://www.ijcopi.org/ojs/article/view/198
Section
Articles

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