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Article

Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model

by
Ru Yu
1,
Xiaoli Wang
1,
Xiaojun Xu
1,* and
Zhiwen Zhang
2
1
School of Economics and Management, Yanshan University, Qinhuangdao 066000, China
2
School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, China
*
Author to whom correspondence should be addressed.
Systems 2024, 12(11), 486; https://doi.org/10.3390/systems12110486
Submission received: 25 September 2024 / Revised: 4 November 2024 / Accepted: 11 November 2024 / Published: 13 November 2024
(This article belongs to the Topic Data-Driven Group Decision-Making)
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Abstract

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales.
Keywords: pure electric vehicle; sales forecasting; combined model theory; seasonal autoregressive integrated moving average model; gray relational analysis; support vector regression model pure electric vehicle; sales forecasting; combined model theory; seasonal autoregressive integrated moving average model; gray relational analysis; support vector regression model

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MDPI and ACS Style

Yu, R.; Wang, X.; Xu, X.; Zhang, Z. Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model. Systems 2024, 12, 486. https://doi.org/10.3390/systems12110486

AMA Style

Yu R, Wang X, Xu X, Zhang Z. Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model. Systems. 2024; 12(11):486. https://doi.org/10.3390/systems12110486

Chicago/Turabian Style

Yu, Ru, Xiaoli Wang, Xiaojun Xu, and Zhiwen Zhang. 2024. "Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model" Systems 12, no. 11: 486. https://doi.org/10.3390/systems12110486

APA Style

Yu, R., Wang, X., Xu, X., & Zhang, Z. (2024). Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model. Systems, 12(11), 486. https://doi.org/10.3390/systems12110486

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