Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 50 , 01 December 2023
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Access | Article
China’s Urban and Rural CPI Prediction Based on ARIMA Model
* Author to whom correspondence should be addressed.
Abstract
Inflation represents the continuous rise of the overall price level of a country. In severe cases, it may cause an imbalance between social supply and demand and lead to a crisis of currency confidence. Therefore, it is necessary to measure and predict the level of inflation. The CPI index is an important indicator to measure the level of inflation, which can largely reflect the national economic situation in a certain period. This paper conducts research by selecting the urban and rural CPI data of the National Bureau of Statistics from January 2007 to June 2023, a total of 198 months. After data processing and inspection, this paper use ARIMA model to forecast. The experimental research results show that the ARIMA (12,0,1) model and the ARIMA (12,0,0) model have good predictive effects on the CPI index of cities and villages respectively. In the short term, the ARIMA model can accurately predict the changing trend of the CPI index, with an error rate of less than 0.5%. The model predicts that China's urban and rural inflation from June 2023 to June 2024 will be stable and improving overall.
Keywords
Time-Series analysis, CPI prediction, ARIMA model
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-147-6
- ISBN (Online)
- 978-1-83558-148-3
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/50/20230559
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited