Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)

Application of ARIMA Model in the Prediction of the Gross Domestic Product

Authors
Bing Yang, Chenggang Li, Min Li, Kang Pan, Di Wang
Corresponding Author
Bing Yang
Available Online December 2016.
DOI
https://doi.org/10.2991/mcei-16.2016.257How to use a DOI?
Keywords
GDP; ARIMA model; Forecast; Residual
Abstract

GDP is an important index that is used to reflect the economy development and people's income. This paper chooses the annual data of Chinese GDP from 1978 to 2014 as the research object, and establishes ARIMA (2, 4, 2) model by applying the Eviews6.0 software. Then, the paper applies this model to forecast the GDP of the following five years, and compares the forecast values with the actual values. The result shows that this model is effective to forecast the GDP in a short term. In the end, the GDP of the following year is forecasted.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
Series
Advances in Intelligent Systems Research
Publication Date
December 2016
ISBN
978-94-6252-282-4
ISSN
1951-6851
DOI
https://doi.org/10.2991/mcei-16.2016.257How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Bing Yang
AU  - Chenggang Li
AU  - Min Li
AU  - Kang Pan
AU  - Di Wang
PY  - 2016/12
DA  - 2016/12
TI  - Application of ARIMA Model in the Prediction of the Gross Domestic Product
BT  - Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
PB  - Atlantis Press
SP  - 1258
EP  - 1262
SN  - 1951-6851
UR  - https://doi.org/10.2991/mcei-16.2016.257
DO  - https://doi.org/10.2991/mcei-16.2016.257
ID  - Yang2016/12
ER  -