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Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China

Author

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  • Yong Zhang
  • Miner Zhong
  • Nana Geng
  • Yunjian Jiang

Abstract

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.

Suggested Citation

  • Yong Zhang & Miner Zhong & Nana Geng & Yunjian Jiang, 2017. "Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-15, May.
    • Handle: RePEc:plo:pone00:0176729
    DOI: 10.1371/journal.pone.0176729
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    2. Minfeng Wu & Wen Chen, 2022. "Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN," Sustainability, MDPI, vol. 14(4), pages 1-14, February.
    3. Jian Huang & Qinyu Chen & Chengqing Yu, 2022. "A New Feature Based Deep Attention Sales Forecasting Model for Enterprise Sustainable Development," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    4. Jia Yao & Siqin Xiong & Xiaoming Ma, 2020. "Comparative Analysis of National Policies for Electric Vehicle Uptake Using Econometric Models," Energies, MDPI, vol. 13(14), pages 1-18, July.
    5. Min Zhao & Yu Fang & Debao Dai, 2023. "Forecast of the Evolution Trend of Total Vehicle Sales and Power Structure of China under Different Scenarios," Sustainability, MDPI, vol. 15(5), pages 1-22, February.

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