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

Zhang, Yong; Zhong, Ming; Geng, Nan; Jiang, Yunjian

doi:10.1371/journal.pone.0176729

Cited by 41 publications

(30 citation statements)

References 51 publications

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“…The article goes further in time, and predicts the 64% of sales and 24% of the fleet (i.e., around 2.8 million) will be EVs by 2030. Other work is Zhang et al [83]. This research uses multivariate and univariate time-series models for forecast based on the 60-month sales data in China, from January 2011, to December 2015.…”

Section: Ev Future Salesmentioning

confidence: 99%

Electric Vehicles: A Data Science Perspective Review

2019

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Current trends are showing that the popularity of electric vehicles (EVs) has significantly increased over the last few years, causing changes not only in the transportation industry but generally in business and society. This paper covers one possible angle to the (r)evolution instigated by EVs, i.e., it provides the data science perspective review of the interdisciplinary area at the intersection of green transportation, energy informatics, and economics. Namely, the review summarizes data-driven research in EVs by identifying two main research streams: (i) socio–economic, and (ii) socio–technical. The socio–economic stream includes research in: (i) acceptance of green transportation in countries and among different populations, (ii) current trends in the EV market, and (iii) forecasting future sales for the green transportation. The socio–technical stream includes research in: (i) electric vehicle battery price and capacity and (ii) charging station management. This kind of study is especially important now when the question is no longer whether the transition from internal-combustion engine vehicles to clean-fuel vehicles is going to happen but how fast it will happen and what are going to be implications for society, governmental policies, and industry. Based on the presented literature review, the paper also outlines the most significant open questions and challenges that are yet to be solved: (i) scarcity of trustworthy (open) data, and (ii) designing a generalized methodology for charging station deployment.

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Section: Ev Future Salesmentioning

confidence: 99%

Electric Vehicles: A Data Science Perspective Review

2019

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“…The higher index of CPI means that the cash they hold is worth less. In that case, people may be less willing to try New-energy Vehicles with high inflation (Zhang, 2017). H3: CPI has negative effect on sales of New-energy Vehicles.…”

Section: Proposed Modelmentioning

confidence: 99%

Predict the sales of New-energy Vehicle using linear regression analysis

Tang¹,

Sun

2019

E3S Web Conf.

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New-energy Vehicle is a new form of vehicle that uses environmental friendly energy and becomes a popularity in recent years. It is important for both the consumers and the producers to search for factors that might affect the sales of New-energy Vehicle. In this study, three linear regression models are examined to determine factors that have significant effect on sales of New-energy Vehicle. ANOVA analysis is conducted to test the model validity and to compare the effect of the three models obtained. Result shows that disposable income, number of charging station, subsidies and literacy level positively affect the sales of New-energy Vehicle.

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“…Akaike information criteria (AIC) and Schwarz criterion (SC) are commonly used to select the number of lag to determine p value in this article. [20][21][22] The principle of the method determining the p value tries to make sure that the values of AIC and SC are minimal. The null hypothesis of the ADF is that there is one unit root at least, and the alternative hypothesis is that the sequence does not have a unit root.…”

Section: Unit Root Testmentioning

confidence: 99%

Chinese automobile sales forecasting using economic indicators and typical domestic brand automobile sales data: A method based on econometric model

Gao

Xie

Cui

et al. 2018

Advances in Mechanical Engineering

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Accurate sales forecasting plays an increasingly important role in automobile companies due to fierce market competition. In this article, an econometric model is proposed to analyze the dynamic connections among Chinese automobile sales, typical domestic brand automobile (Chery) sales, and economic variables. Four tests are required before modeling, which include unit root, weak exogeneity, cointegration, and Granger-causality test. The selected economic variables consist of consumer confidence index, steel production, consumer price index, and gasoline price. Monthly is used to empirical analysis and the result shows that there is long-term cointegration relationship between Chinese automobile sales and the endogenous variables. A vector error correction model in econometric based on cointegration is applied to quantify long-term impact of endogenous variables on Chinese automobile sales. Compared with other classical timeseries methods, root mean square error (0.1243) and mean absolute percentage error (10.2015) by vector error correction model for 12-period forecasting are minimal. And the forecasting result is better when the impact of Chery sales is considered. That means that the fluctuation trends of Chinese automobile sales and typical domestic brand automobile sales are closely linked.

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

Cited by 41 publications

References 51 publications

Electric Vehicles: A Data Science Perspective Review

Electric Vehicles: A Data Science Perspective Review

Predict the sales of New-energy Vehicle using linear regression analysis

Chinese automobile sales forecasting using economic indicators and typical domestic brand automobile sales data: A method based on econometric model

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