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

I tiakina i:
I whakaputaina i:PLoS One vol. 12, no. 5 (May 2017), p. e0176729
Kaituhi matua: Zhang, Yong
Ētahi atu kaituhi: Miner, Zhong, Geng, Nana, Jiang, Yunjian
I whakaputaina:
Public Library of Science
Ngā marau:
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
Urunga tuihono:Citation/Abstract
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Whakarāpopotonga: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
Puna:Health & Medical Collection