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Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm

Author

Listed:
  • Fei Qu

    (School of Business, Guilin University of Technology, Guilin 541004, China)

  • Yi-Ting Wang

    (School of Business, Central South University, Changsha 410083, China)

  • Wen-Hui Hou

    (School of Business, Central South University, Changsha 410083, China)

  • Xiao-Yu Zhou

    (School of Business, Central South University, Changsha 410083, China)

  • Xiao-Kang Wang

    (School of Business, Central South University, Changsha 410083, China)

  • Jun-Bo Li

    (School of Business, Guilin University of Technology, Guilin 541004, China)

  • Jian-Qiang Wang

    (School of Business, Central South University, Changsha 410083, China)

Abstract

With the development of the Internet and big data, more and more consumer behavior data are used in different forecasting problems, which greatly improve the performance of prediction. As the main travel tool, the sales of automobiles will change with the variations of the market and the external environment. Accurate prediction of automobile sales can not only help the dealers adjust their marketing plans dynamically but can also help the economy and the transportation sector make policy decisions. The automobile is a product with high value and high involvement, and its purchase decision can be affected by its own attributes, economy, policy and other factors. Furthermore, the sample data have the characteristics of various sources, great complexity and large volatility. Therefore, this paper uses the Support Vector Regression (SVR) model, which has global optimization, a simple structure, and strong generalization abilities and is suitable for multi-dimensional, small sample data to predict the monthly sales of automobiles. In addition, the parameters are optimized by the Grey Wolf Optimizer (GWO) algorithm to improve the prediction accuracy. First, the grey correlation analysis method is used to analyze and determine the factors that affect automobile sales. Second, it is used to build the GWO-SVR automobile sales prediction model. Third, the experimental analysis is carried out by using the data from Suteng and Kaluola in the Chinese car segment, and the proposed model is compared with the other four commonly used methods. The results show that the GWO-SVR model has the best performance of mean absolute percentage error (MAPE) and root mean square error (RMSE). Finally, some management implications are put forward for reference.

Suggested Citation

  • Fei Qu & Yi-Ting Wang & Wen-Hui Hou & Xiao-Yu Zhou & Xiao-Kang Wang & Jun-Bo Li & Jian-Qiang Wang, 2022. "Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm," Mathematics, MDPI, vol. 10(13), pages 1-22, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2234-:d:848077
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    References listed on IDEAS

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    Cited by:

    1. Tendai Makoni & Delson Chikobvu, 2023. "Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on New Car Sales in South Africa," Data, MDPI, vol. 8(5), pages 1-16, April.
    2. Wang, Ning & Shang, Kai & Duan, Yan & Qin, Dandan, 2023. "Carbon quota allocation modeling framework in the automotive industry based on repeated game theory: A case study of ten Chinese automotive enterprises," Energy, Elsevier, vol. 279(C).

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