Abstract
This work shows the results of forecasting the behavior of sales in the Mexican automotive industry in a simulated scenario without COVID-19 and comparing it with the actual sales numbers. As this pandemic has caused traditional forecasting techniques to show poor performance and low prediction quality, this work aims to estimate the number of sales lost during the pandemic, using a machine learning model based on several explanatory variables and predicting those variables without the influence of the COVID-19 pandemic. Three different regression models were tested (Linear regression, Random Forest and Neural Network) creating scenarios and incorporating different variables into the models. Random Forest with 3 variables shows the highest predictive power. This model applied on forecast variables without pandemic’s impact predicts 1,342,028 units sold between February 2020 and January 2021, representing a 29.76% drop in sales and a total impact of 416,324 sales lost due to the pandemic.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Botchkarev, A.: Performance metrics (error measures) in machine learning regression, forecasting and prognostics: properties and typology. Interdisc. J. Inf. Knowl. Manag. 14, 45–79 (2018). https://doi.org/10.28945/4184
Deloitte: Perspectiva Industrial - Industria Automotriz (2020). https://www2.deloitte.com/content/dam/Deloitte/mx/Documents/finance/2020/Perspectiva-Industria-Automotriz-DEconosignal.pdf
González, L.: Sales of Cars in Mexico Decreased 28 Percent in 2020. The Economist , Westminster (2021)
Heath, J.: Lo Que Indican Los Indicadores (2020). http://internet.contenidos.inegi.org.mx/contenidos/Productos/prod_serv/contenidos/espanol/bvinegi/productos/estudios/indican_indi/indica_v25iv12.pdf
Ho, T.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282 (1995). https://doi.org/10.1109/ICDAR.1995.598994
Idris, N.O., Achban, A., Utiarahman, S.A., Karim, J., Pontoiyo, F.: Predicting the selling price of cars using business intelligence with the feed-forward backpropagation algorithms. In: 2020 Fifth International Conference on Informatics and Computing (ICIC), pp. 1–6 (2020). https://doi.org/10.1109/ICIC50835.2020.9288594
INEGI: Programa Anual de Investigación del INEGI (2021). https://sc.inegi.org.mx/repositorioNormateca/Od_28Ene21.pdf (2021)
Lozano, L.F.: Industria Automotriz Espera Mejores Ventas En último Bimestre de (2020). https://www.forbes.com.mx/negocios-industria-automotriz-mejores-ventas-ultimo-bimestre-2020/
Mauricio, J.: Temporal series analysis. In: UCM (2013)
Murtagh, F.: Fionn, m.: Multilayer perceptrons for classification and regression. Neurocomputing 2, 183–197 (1990). https://doi.org/10.1016/0925-2312(91)90023-5
Paz, K.: La evolución en la venta de vehículos en tiempos de COVID 19 (2020). https://www.jato.com/mexico/la-evolucion-en-la-venta-de-vehiculos-en-tiempos-de-covid-19/
Rodríguez, I.: Car sales in Mexico grow for the first time in three years (2020). https://expansion.mx/empresas/2020/03/04/la-venta-de-autos-en-mexico-crece-febrero-2020-amda
Thornton, G.: Boletín de Economía (2020). https://www.grantthornton.mx/globalassets/1.-member-firms/mexico/pdf/boletin-de-economia-febrero-2020-v.1.pdf
Uriel, E.: Introducción a la Econometría (2019). https://www.uv.es/uriel/manual/Introducci%C3%B3n%20a%20la%20econometr%C3%ADa%202012-09-2019%20B.pdf
Wei, W.: Time series regression. In: International Encyclopedia of Statistical Science, pp. 1607–1609 (2011). https://doi.org/10.1007/978-3-642-04898-2_596
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ramírez, J., Alarcón, J., Calzada, G., Ponce, H. (2021). Mexican Automotive Industry Sales Behavior During the COVID-19 Pandemic. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2021. Lecture Notes in Computer Science(), vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-89820-5_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89819-9
Online ISBN: 978-3-030-89820-5
eBook Packages: Computer ScienceComputer Science (R0)