Analysis of Automobile Industry Trends and Demand Forecasting of Monthly Automobile Sales in Chin

중국 내 자동차 산업 동향과 월별 판매량 시계열분석

  • 왕첸양 (부경대학교 일반대학원 경영학과) ;
  • 이세원 (부경대학교 경영학부)
  • Received : 2023.01.18
  • Accepted : 2023.02.08
  • Published : 2023.02.28


In this study, we introduced the development status and the government policy of the Chinese automobile industry under the rapidly changing global economic environment. We conducted a consumer trend survey on automobile purchases by consumers in China. Despite the Chinese government's strong national emission control policy and stricter standards for manufacturing and selling internal combustion engine vehicles, 59.6% of respondents saying they would choose an internal combustion engine vehicle when purchasing a vehicle in the future for various reasons. It was confirmed that there is a significant gap between government policies and consumer perceptions. In addition, we have discovered the recent declining trend of automobile sales in China, and used the monthly sales volume from January 2010 to December 2020 as training set, and the sales volume from January 2021 to November 2022 as a test set. We proposed and evaluated a time-series model for predicting future automobile demand in China. Then, we showed the monthly sales forecast for 2023 when each model was applied.

본 연구에서는 급변하고 있는 세계 경제 환경 하에서 중국 자동차 산업의 발전 현황과 자동차 산업과 관련한 중국 정부의 정책을 살펴보고, 중국 내 소비자들의 자동차 구입에 대해 소비자 동향 조사를 실시하였다. 중국 정부의 강력한 국가 배출가스 규제정책과 내연기관 자동차 제조·판매 기준의 강화에도 불구하고 소비자들은 다양한 이유로 앞으로 자동차를 구매 시 내연기관차를 선택하겠다는 응답비율이 59.6%에 달하는 등 정부 정책과 소비자 인식 사이에는 적지 않은 차이가 존재하고 있음을 확인하였다. 또한, 최근의 중국 내 자동차 판매량의 감소 추세를 발견하여 2010년 1월부터 2020년 12월까지 월별 판매량을 학습용 데이터로, 2021년 1월부터 2022년 11월 동안의 판매량을 평가용으로 구분하여 향후 중국의 자동차 수요를 예측하는 시계열 모형들을 제안, 평가하였다. 그리고 각 시계열모형을 적용하였을 때의 2023년도의 월별 예측 판매량을 보였다.



이 논문은 부경대학교 자율창의학술연구비(2022년)에 의하여 연구되었음


  1. Ahn, B.-K., Choi, Y.-H., Lee, Z.-H. (2014). A Study on Forecasting of Chinese Auto Industry using Series Model. The Northeast Asia Economic Association of Korea, 26(3), 37-62.
  2. Cachon, G., Terwiesch, C., Matching Supply with Demand, McGraw-Hill Education, 2019.
  3. Chen, D. (2011). Chinese Automobile Demand Prediction Based on ARIMA Model. 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI ), 2197-2201.
  4. China Association of Automobile Manufactures,
  5. Gao, J., Xie, Y., Cui, X., Yu, H., Gu, F. (2018). Chinese automobile sales forecasting using economic indicators and typical domestic brand automobile sales data: A method based on econometric model. Advances in Mechanical Engineering, 10(2), 1-11.
  6. Hyndman, R. J. & Athanasopoulos, G., Forecasting: Principles and Practice, OTexts, 2018.
  7. Kaya, A., Kaya, G., Cebi, F. (2019). Forecasting Automobile Sales in Turkey with Artificial Neural Networks. International Journal of Business Analytics, 6(4), 50-60.
  8. Kwahk, K.-Y., Statistical Data Analysis with R, Chungram, 2019.
  9. Shmueli, G. & Lichtendahl, K. C. Jr., Practical Time Series F orecasting with R: A Hands-On-Guide, Axelrod Schnall Publishers, 2016.
  10. Stevenson, W. J. & Chuong, S. C., Operations Management, McGraw-Hill Education, 2014.
  11. Swink, M., Melnyk, S., Cooper, M. B., Hartley, J. L., Managing Operations: Across the Supply chain, McGraw-Hill Education, 2016.
  12. Suh, S. C., Saffer, S. I., Li, D., Gao, J. M. (2004). A new insight into prediction modeling systems. J. of Integrated Design and Process Science, 2, 85-104.
  13. Sana, P. S., Hassan, M. K., Yang, Z. (2017). Annual Automobile Sales Prediction Using ARIMA Model. International Journal of Hybrid Information Technology, 10(6), 13-22.
  14. Wang, C. (2022). Analysis of development trends and demand forecasting of Chinese automobile industry, MBA Thesis, Graduate School of Pukyong National University, Busan, Korea.
  15. Wang, Y., Jacob, T., Daniel, S. (2012). Will China's Vehicle Population Grow Even Faster than Forecasted. ACCESS Magazine, 41, 29-33.
  16. Zeng, M., Zeng, F., Zhu, Q., Xue, S. (2013). Forecast Of Electric Vehicles In China Based On Bass Model. Electric Power, 46(1), 36-39.
  17. Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175