Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China
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| Publikašuvnnas: | PLoS One vol. 12, no. 5 (May 2017), p. e0176729 |
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| Váldodahkki: | |
| Eará dahkkit: | , , |
| Almmustuhtton: |
Public Library of Science
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| Fáttát: | |
| Liŋkkat: | Citation/Abstract Full Text Full Text - PDF |
| Fáddágilkorat: |
Eai fáddágilkorat, Lasit vuosttaš fáddágilkora!
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MARC
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| 001 | 1893857787 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0176729 |2 doi | |
| 035 | |a 1893857787 | ||
| 045 | 2 | |b d20170501 |b d20170531 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Zhang, Yong | |
| 245 | 1 | |a Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China | |
| 260 | |b Public Library of Science |c May 2017 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a China | |
| 653 | |a Gasoline | ||
| 653 | |a Bioinformatics | ||
| 653 | |a Computer applications | ||
| 653 | |a Optimal control | ||
| 653 | |a Anxiety | ||
| 653 | |a Electricity | ||
| 653 | |a Air pollution | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Cost analysis | ||
| 653 | |a Mapping | ||
| 653 | |a Economic | ||
| 653 | |a Vehicles | ||
| 653 | |a Information processing | ||
| 653 | |a Energy | ||
| 653 | |a Environmental effects | ||
| 653 | |a Statistics | ||
| 653 | |a Fuels | ||
| 653 | |a Finance | ||
| 653 | |a Automobiles | ||
| 653 | |a Electric vehicles | ||
| 653 | |a Accounting | ||
| 653 | |a Passengers | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Indicators | ||
| 653 | |a Transportation | ||
| 653 | |a Economics | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Time series | ||
| 653 | |a Energy policy | ||
| 653 | |a Data processing | ||
| 653 | |a Computer programs | ||
| 653 | |a Informatics | ||
| 653 | |a Applied statistics | ||
| 653 | |a Diffusion | ||
| 653 | |a Noise measurement | ||
| 653 | |a Carbon dioxide | ||
| 653 | |a Noise reduction | ||
| 653 | |a Models | ||
| 653 | |a Mathematical models | ||
| 653 | |a Couplings | ||
| 653 | |a Neural networks | ||
| 653 | |a Rainfall | ||
| 653 | |a Vehicle emissions | ||
| 653 | |a Sales | ||
| 653 | |a Alternative fuel vehicles | ||
| 653 | |a Markets | ||
| 653 | |a Forecasting | ||
| 700 | 1 | |a Miner, Zhong | |
| 700 | 1 | |a Geng, Nana | |
| 700 | 1 | |a Jiang, Yunjian | |
| 773 | 0 | |t PLoS One |g vol. 12, no. 5 (May 2017), p. e0176729 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/1893857787/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/1893857787/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/1893857787/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |