Research On the Forecasting of Sales Volume of New Energy Vehicles Based on SARIMA Model

Jiageng Chen; Yujun Huang; Han Jin; Xiaofeng Chen

doi:10.54097/4rnbz043

Authors

Jiageng Chen
Yujun Huang
Han Jin
Xiaofeng Chen

DOI:

https://doi.org/10.54097/4rnbz043

Keywords:

SARIMA model, new energy vehicle sales, Time series forecasting, Trend and Seasonality Analysis, Forecasting Accuracy.

Abstract

In recent years, the new energy vehicle market has experienced rapid growth due to policy support and environmental enhancements; however, sales volume fluctuates due to various factors, making precise forecasting essential for production planning, supply chain management, and market strategy. This paper conducts a comprehensive forecasting analysis of new energy vehicle sales volume utilizing the SARIMA model, including the fluctuations in the worldwide new energy vehicle industry. The research gathers and preprocesses monthly sales volume data of new energy cars from January 2022 to April 2024 and develops a SARIMA prediction model by thoroughly considering the trend, seasonal, abrupt, and stochastic error components. The projections indicate that the sales volume of new energy cars will attain 478,000 units in May 2024, increase to 883,000 units in June, and see a minor decline to 588,000 units in July, while still being elevated. This particular numerical conclusion offers a scientific foundation for the future advancement of the new energy vehicle market and is highly significant in directing the optimal placement of charging facilities, assessing the adequacy of existing charging infrastructure, and recommending optimal locations for charging station installations.

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References

[1] Jiang Jiachen, Mei Yuxin, Pan Yaoyao, Wang Jie. Development Assessment and Sales Forecast of New Energy Vehicles [J] Journal of Taizhou College. 2024; 46(03):9-15.

[2] Li Wenyuan. New Energy Vehicle Sales Forecast and Influencing Factors Analysis [J] Operator. 2024; (12):92-93.

[3] Wang SK, Liu JH. Research on new energy vehicle sales forecasting based on time series analysis [J] Statistics and Management. 2022; 37(07):88-97.

[4] Zou, Rui, Liu, Jihua, Xu, Si-wei. Forecasting sales volume of new energy vehicles based on time series - A case study of BYD [J] Technology and Industry. 2024; 24(15):87-93.

[5] Yu Mingyang, Shen Bin. Analysis of New Energy Vehicle Sales under Variable International Circumstances - A Compound Forecasting Model Based on Sudden Factors [J] China Business Journal. 2023; (12):164-168.

[6] Xie Jiayin. New energy vehicle SUV sales forecast based on ARIMA promotes the formation of new quality productivity [J] China Economic and Trade Review. 2024; (10):136-138.

[7] Chen Kexiu, Liu Juan. Forecasting new energy vehicle sales of Ola Black Cat based on ARIMA model [J] Modern Industrial Economy and Informatisation. 2022; 12(03):169-171.

[8] Ouyang Di, Cui Jia. Spatial and temporal dynamics of air pollutants in Harbin under the carbon neutral strategy based on the Holt-Winters time series model [J] Environmental Protection Science. 2023; 49(02):112-119.

[9] Wang Ying, Han Baoming, Zhang Qi, Li Dewei. Prediction of inbound passenger flow of Beijing underground based on SARIMA model [J] Transportation Systems Engineering and Information. 2015; 15(06):205-211.

[10] Sun Xianghai, Liu Tanqiu. Short-term Traffic Flow Prediction on Urban Roads Based on SARIMA Model [J] Highway Traffic Science and Technology. 2008; (01):129-133.

[11] Li Jie, Peng Qiyuan, Yang Yuxiang. Passenger flow prediction of Guangzhou-Zhuhai Intercity Railway based on SARIMA model [J] Journal of Southwest Jiaotong University. 2020; 55(01):41-51.