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

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Dades bibliogràfiques
Publicat a:	PLoS One vol. 12, no. 5 (May 2017), p. e0176729
Autor principal:	Zhang, Yong
Altres autors:	Miner, Zhong, Geng, Nana, Jiang, Yunjian
Publicat:	Public Library of Science
Matèries:	China Gasoline Bioinformatics Computer applications Optimal control Anxiety Electricity Air pollution Bayesian analysis Cost analysis Mapping Economic Vehicles Information processing Energy Environmental effects Statistics Fuels Finance Automobiles Electric vehicles Accounting Passengers Spectrum analysis Indicators Transportation Economics Statistical analysis Time series Energy policy Data processing Computer programs Informatics Applied statistics Diffusion Noise measurement Carbon dioxide Noise reduction Models Mathematical models Couplings Neural networks Rainfall Vehicle emissions Sales Alternative fuel vehicles Markets Forecasting
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Descripció
Resum:	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.
ISSN:	1932-6203
DOI:	10.1371/journal.pone.0176729
Font:	Health & Medical Collection