Proceedings of the 8th International Conference on Management and Computer Science (ICMCS 2018)

Analysis and Forecast of Shanghai Financial Revenue Based on Data Mining

Authors
Wei Deng
Corresponding Author
Wei Deng
Available Online October 2018.
DOI
10.2991/icmcs-18.2018.31How to use a DOI?
Keywords
Shanghai fiscal revenue; Adaptive-Lasso; Grey prediction; Neural network
Abstract

After 1994, the fiscal management system that China began to implement was a tax-sharing system. Local fiscal revenue was an important fund for local governments to carry out macroeconomic regulation and control. Based on the data of Shanghai's fiscal revenue and its influencing factors from 1994 to 2016, based on the Adaptive-Lasso variable selection method, the combination of grey prediction and neural network is used to fit and predict Shanghai's fiscal revenue. Providing data support for the Shanghai Municipal Government, and also providing reference for other cities with faster economic development.

Copyright
© 2018, 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 8th International Conference on Management and Computer Science (ICMCS 2018)
Series
Advances in Computer Science Research
Publication Date
October 2018
ISBN
978-94-6252-590-0
ISSN
2352-538X
DOI
10.2991/icmcs-18.2018.31How to use a DOI?
Copyright
© 2018, 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  - Wei Deng
PY  - 2018/10
DA  - 2018/10
TI  - Analysis and Forecast of Shanghai Financial Revenue Based on Data Mining
BT  - Proceedings of the 8th International Conference on Management and Computer Science (ICMCS 2018)
PB  - Atlantis Press
SP  - 157
EP  - 161
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmcs-18.2018.31
DO  - 10.2991/icmcs-18.2018.31
ID  - Deng2018/10
ER  -