Abstract
The new energy vehicles (NEVs) industry has been regarded as the primary industry involving in the transformation of the China automobile industry and environmental pollution control. Based on the quarterly fluctuation characteristics of NEVs’ sales volume in China, this research puts forwards a data grouping approach-based nonlinear grey Bernoulli model (DGA-based NGBM (1,1)). The main ideas of this work are to effectively predict quarterly fluctuation of NEVs industry by introducing a data grouping approach into the NGBM (1,1) model, and then use the particle swarm optimization (PSO) algorithm to optimize the parameters of the model so as to increase forecasting precision. By empirical comparison between the DGA-based NGBM (1,1) and existing data grouping approach-based GM (1,1) model (DGA-based GM (1,1)), DGA-based NGBM (1,1) can effectively reduce the prediction error resulting from quarterly fluctuation of sales volume of the NEVs, and prediction performance are proven to be favorable. The results of out-of-sample forecasting using the model proposed show that the sales volume of NEVs in China will increase by 57% in 2019–2020 with a quarterly fluctuation. In 2020, the sales volume of NEVs will exceeds the target of 2 million in the “13th Five-Year Strategic Development Plan”. Therefore, China needs to pay more attention to infrastructure construction and after-sales service for NEVs.
Highlights
Background and MotivationAutomobile industry, as a pillar industry in national economy, plays an essential role in the development of economy and society
The contributions of this paper include the following two aspects: (1) This paper proposed a data grouping approach-based nonlinear grey Bernoulli model (DGA-based NGBM (1,1)) which can effectively predict the quarterly fluctuations of quarterly sales of the new energy vehicle industry in China
By conducting empirical comparison between the DGA-based NGBM (1,1) and existing DGA-based GM (1,1), When the time series has nonlinear characteristics of quarterly fluctuation, data grouping approach can effectively identify the quarterly difference, it can improve the fitting accuracy, at the same time, the particle swarm optimization (PSO) algorithm optimizes the power exponent and background value of the DGA-based NGBM (1,1) model, which can flexibly fit the nonlinear trend of data, so the DGA-based NGBM (1,1) is able to reduce the predicating error triggered by quarterly fluctuation of sales volume of the new energy vehicles (NEVs), showing high predicating performance
Summary
Automobile industry, as a pillar industry in national economy, plays an essential role in the development of economy and society. The Decision on Rapidly Fostering and Developing Strategic Emerging Industries and The Development Plan of Energy-saving New Energy Vehicles Industry during 2012–2020 were issued by the State Council of China in 2010 and 2012, respectively. The data issued by China Association of Automobile Manufactures show that the sales volumes of NEVs during 2012–2016 are 12,791, 17,600, 74,763, 331,092, and 507,000 and total sales volume within the five-year increase by 41-fold [2]. 22 ooff 1155 the sales volume of NEVs in 2016 takes up 1.81% of total sales volume of vehicles in China in the usapm1.e81p%erioofdtoatnaldsainlecsrevaosleusmbeyo5f3v%eh, ipcrleseintCinhginaarianptihdegsraomwethp.erAiocdcoarnddinignctroeaCsheisnbay’s5133%t,hpFreivse-nYtienagr aStrraaptiedggicroDwetvhe.lAopcmcoerndtinPgltaonC, hthinea’ssa1le3sthvFoilvue-mYeearofStNraEteVgsicwDielvleilnopcmreeanstePtloanm, tohreesathleasnvtowluomme iollfiNonEVbys w20il2l0i.ncInreaosredteormtooraecthhiaenvetwtohemoilvlieornalblyg2o0a2l0s. Xu and Zhao [13] employed an equal dimension gray recurrence model to predict the patent application quantity in the NEV industry in the 13th Five-Year-Plan period. Results indicate that: the patent application quantity of China’s NEVs industry during the 13th Five-Year Plan period will be expected to present a great increase, which showing that NEVs industry within five years will be remarkably developed
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