지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.10
- 수록면
- 63 - 72 (10page)
- DOI
- 10.25052/KSCM.2021.10.21.2.63
이용수
초록· 키워드
오류제보하기
The Government are always making various efforts to establish sound financial management and stable budget policies.. In particular, the work related to the fund plan and the operation plan of the treasury through manual work is subject to improvement, and a financial estimation system is needed to achieve it.. In this study, we wanted to analyze whether it is possible to predict the balance of the treasury by utilizing machine learning. First of all, we developed a forecasting model for treasury balances. In addition, to verify the applicability, the government balance predictability of machine learning techniques, performance verification, and major variable verification were performed. In detail, data collection and refinement, exploratory analysis, analytical model design and development, and analysis result review were studied. The analysis target data is 120 months of total data from 2010 to 2019, limited to income tax and value-added tax revenue forecasts, with monthly revenue volume and major taxes. This was predicted for 2019 tax revenue respectively. The application algorithms are Linear Regression, Random Forest, and Gradient Boosting. According to the analysis, Linear Regression was the best in revenue and income taxes, while Gradient Boosting was the best in VAT. In conclusion, it was analyzed that machine learning was applicable to treasury predictions. This will allow quick results, scientific analysis through the introduction of big data-based models, and the person in charge will be able to improve predictive power through new model tests. It is also expected that the government"s confidence in fiscal management will be improved.
목차
1. 서론
2. 국고잔액 예측 분석 프로세스
3. 분석결과 및 주요 특성
4. 결론 및 향후 연구
REFERENCES
참고문헌 (10)
참고문헌 신청
Ahn, J. J. (2017). Examples of Seoul's Administrative Services Using Big Data.
Choi, B. & Yoon, J. J. (2018). Seoul Public Big Data Revitalization Plan, Policy Task. Research Report.
Lee, H. (2020). Composition and operation status of digital budget accounting system. Monthly Nara Finance, 39, 12-19.
Jang, H. & Kim, J. (2017). A Machine Learning Approach for Mechanical Motor Fault Diagnosis. J. Soc. Korea Ind. Syst. Eng, 40(1), 57-64.
Jung, j. H. (2020). Big Data Platform Operational Status and Improvement Tasks. National Assembly Legislative Investigation Office, Legislative Policy Report, 55.
Choi, B. & Yoon, J. J. (2018). Seoul Public Big Data Revitalization Plan, Policy Task. Research Report.
Lee, H. (2020). Composition and operation status of digital budget accounting system. Monthly Nara Finance, 39, 12-19.
Jang, H. & Kim, J. (2017). A Machine Learning Approach for Mechanical Motor Fault Diagnosis. J. Soc. Korea Ind. Syst. Eng, 40(1), 57-64.
Jung, j. H. (2020). Big Data Platform Operational Status and Improvement Tasks. National Assembly Legislative Investigation Office, Legislative Policy Report, 55.
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