Content area
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.
Details
Bioinformatics;
Computer applications;
Optimal control;
Anxiety;
Electricity;
Air pollution;
Bayesian analysis;
Cost analysis;
Mapping;
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