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

Yong Zhang, Miner Zhong, Nana Geng and Yunjian Jiang

PLOS ONE, 2017, vol. 12, issue 5, 1-15

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0176729

DOI: 10.1371/journal.pone.0176729

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