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21 July 2024 Analysis and forecasting research on China's new energy electric vehicle industry based on ARIMA model and multiple linear regression model
Wenjing Ma, Qingyu Yang, Huanzhen Li, Yanqing Shen
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Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132194J (2024) https://doi.org/10.1117/12.3036634
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China
Abstract
New energy electric vehicles are an important direction for promoting sustain- able development in the context of the global energy crisis and increasingly severe environmental pollution problems. This article combines relevant statistical knowledge and uses multiple linear regression model, ARIMA model to conduct in-depth research on the topic of “new energy electric vehicles”. In this study, we set up a multiple linear regression model to analyse the factors related to the development of new energy vehicles in China, and conclude that the number of charging piles, genset production, buses production, lithiumion batteries is closely associated with China’s new energy electric vehicles’ development. And we use ARIMA time series model in modelling and forecasting the sales volume of new energy vehicles in China for the next decade. The statistical analysis was performed by the use of time plot to display the trend of the data, Autocorrelation Function (ACF), Partial Autocorrelation Functions (PACF). Our result shows that ARIMA (1, 2, 1) is the best ARIMA model to forecast annual production and we also find that the sales volume of new energy vehicles in China is expected to reach 16.98 million, reflecting the rapid development and growth of the new energy vehicle industry in China.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Wenjing Ma, Qingyu Yang, Huanzhen Li, and Yanqing Shen "Analysis and forecasting research on China's new energy electric vehicle industry based on ARIMA model and multiple linear regression model", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132194J (21 July 2024); https://doi.org/10.1117/12.3036634
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KEYWORDS
Industry
Data modeling
Linear regression
Analytical research
Statistical modeling
Statistical analysis
3D modeling
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