Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)

Research on GDP Forecast of the Inner Mongolia Autonomous Region Based on ARIMA Model

Authors
Zihan Zhao1, *
1School of Information Qingdao No.2 middle school, Qingdao, Shandong 193000, China
*Corresponding author. Email: Isabella03254@163.com
Corresponding Author
Zihan Zhao
Available Online 15 December 2021.
DOI
10.2991/assehr.k.211209.430How to use a DOI?
Keywords
GDP; ARIMA Model; Inner Mongolia; Time series forecasting
Abstract

This study focuses on the GDP development and forecast of Inner Mongolia Autonomous Region from 1993 to 2020. ARIMA model and time series forecasting are built to analyze the population development and forecast the GDP index in the next five years. The results show that the GDP of Inner Mongolia Autonomous Region will further increase to some extent, and these data can be regarded as a good assistant for the government of Inner Mongolia Autonomous Region in planning the next stage of economic development. And guide the government to further planning and development.

Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)
Series
Advances in Economics, Business and Management Research
Publication Date
15 December 2021
ISBN
10.2991/assehr.k.211209.430
ISSN
2352-5428
DOI
10.2991/assehr.k.211209.430How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zihan Zhao
PY  - 2021
DA  - 2021/12/15
TI  - Research on GDP Forecast of the Inner Mongolia Autonomous Region Based on ARIMA Model
BT  - Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)
PB  - Atlantis Press
SP  - 2648
EP  - 2652
SN  - 2352-5428
UR  - https://doi.org/10.2991/assehr.k.211209.430
DO  - 10.2991/assehr.k.211209.430
ID  - Zhao2021
ER  -