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Title:

Forecasting Electric Vehicle Sales with Univariate and Multivariate Time Series Models: The Case of China

Accession Number:

01624269

Record Type:

Component

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 market-specific exogenous parameters 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.

Supplemental Notes:

This paper was sponsored by TRB committee ADC80 Standing Committee on Alternative Transportation Fuels and Technologies.

Monograph Accession #:

01618707

Report/Paper Numbers:

17-03184

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Zhang, Yong
Zhong, Miner
Jiang, Yunjian

Pagination:

15p

Publication Date:

2017

Conference:

Transportation Research Board 96th Annual Meeting

Location: Washington DC, United States
Date: 2017-1-8 to 2017-1-12
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Geographic Terms:

China

Subject Areas:

Economics; Highways; Planning and Forecasting; Vehicles and Equipment

Source Data:

Transportation Research Board Annual Meeting 2017 Paper #17-03184

Files:

TRIS, TRB, ATRI

Created Date:

Dec 8 2016 11:11AM