Analysis and Forecast of New Energy Vehicle Sales Volume Based on Industry Data under the Epidemic
Abstract
In daily life, the cars play an important role as a means of transportation. However, economies of many fields have been indelibly
hit this year due to the pandemic. In order to help automobile manufacturers, consumers and the government take countermeasures in case
of similar man-made or natural disasters in the future, this paper will predict the future automobile sales based on China’s GDP data in 2020
and performance source of automobile‘s parameters and sales volume in the two years before and after the epidemic. Previous studies on car
sales mostly focus on future sustainability development in the industry, but there is a lack of research using data and quantitative analysis.
This paper will use data processing and machine learning to predict the sale volume as well as finding the key factors that will influence the
sale volume.
hit this year due to the pandemic. In order to help automobile manufacturers, consumers and the government take countermeasures in case
of similar man-made or natural disasters in the future, this paper will predict the future automobile sales based on China’s GDP data in 2020
and performance source of automobile‘s parameters and sales volume in the two years before and after the epidemic. Previous studies on car
sales mostly focus on future sustainability development in the industry, but there is a lack of research using data and quantitative analysis.
This paper will use data processing and machine learning to predict the sale volume as well as finding the key factors that will influence the
sale volume.
Keywords
Statistics; Pandemic; Vehicle Sale; Finance
Full Text:
PDFReferences
[1] Cai B, Rui MJ. Research on Automobile Sales Forecasting in China based on improved differential Evolution Algorithm and Grey
Model [J]. Shanghai Management Science, 2015, 37(01):14-20.
[2] Zhu LR, Ge DD, Pan LD. Response analysis of new energy vehicle industry chain based on the COVID-19 epidemic [J]. Southern
Agricultural Machinery, 2019, 51(23): 44+58.
[3] Schoenfeld David. (1982). Partial residuals for the proportional hazards regression model. Biometrika.
DOI: http://dx.doi.org/10.18686/fm.v8i6.11711
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