Academic Journal of Computing & Information Science, 2022, 5(10); doi: 10.25236/AJCIS.2022.051005.
Hongye Cai, Wenxuan Qiu
College of Mathematics and Statistic, Shenzhen University, Shenzhen, 518060, China
GDP can measure the development level and economic situation of a region to a certain extent. This paper selects the GDP data of Shenzhen from 1980 to 2020, and constructs the Arima(2, 2, 3) model to predict the GDP data in the next 5 years. First, after Box-Cox transformation and difference operation are performed on the selected data, the Augmented Dickey-Fuller (ADF) stationarity test is carried out. Under the condition of passing the ADF test, this paper uses the maximum likelihood method to iteratively estimate the parameters of the model based on the initial value of the least squares’ estimation, and then performs a mixed test on the residual data to verify the validity of the parameters. Finally, the h steps forward method is used to predict the GDP data of Shenzhen in the next five years, and the corresponding confidence interval is obtained. The relative error between the prediction results in 2021 and the actual data is 2.9%, indicating that the prediction results of the model are good and have certain feasibility.
Shenzhen GDP; ARIMA model; R Studio; Forecast results
Hongye Cai, Wenxuan Qiu. Prediction Analysis of Shenzhen GDP Based on ARIMA Model and Implementation in R Language. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 10: 28-34. https://doi.org/10.25236/AJCIS.2022.051005.
 Guo Zhiwu, Pu Jihong, Teng Guozhao. Study on the Method of Chinese New Year Factor’s Adjustment Based on ARIMA model [J]. China Health Statistics, 2009,26 (6):573-576,579.
 Zheng Huimin, Xue Yunlian, Huang Yanfei, et al. Application of ARIMA model to predicting the incidence tendency of notifiable communicable diseases in Shenzhen City [J]. Practical Preventive Medicine, 2016, 23(2): 240-243.
 Yan Zhouning, Mu Jingfeng, Zhao Xing, et al. The time series prediction of PM2.5 in Shenzhen based on ARIMA model [J]. Modern Preventive Medicine, 2018, 45(2):220-223,242.
 Yi Zhiguo, Xiang Lili. Research on cigarette sales forecast based on ARIMA model [J]. Chinese and Foreign Entrepreneurs, 2021(11):149.
 Chen Congcong. Prediction and Analysis of Shandong Province's GDP Based on ARIMA Model and ARIMAX Model [D]. Shandong University, 2016.
 Yan Yanwen. Analysis and forecast of GDP in Shandong Province Based on ARIMA Model [J]. Mathematics in Practice and Theory, 2018,48(04):285-292.
 Zhao Zimeng. Predicting Chengdu's GDP based on ARIMA time series model [J]. Communication World, 2019, 26(02): 206-207.
 Xu Mingyan. Prediction and Analysis of Jiangsu Province's GDP Based on ARIMA Model and BP Neural Network Model [D]. Shandong University, 2020.
 Qu Haiqing, He Xianping. Research on GDP forecasting model of Hubei Province based on time series analysis [J]. Journal of Hubei University of Economics (Humanities and Social Sciences), 2021, 18(09): 37-39.
 Xie Zhikui, Li Zhuo. Shenzhen Model: World Trend and Chinese Characteristics: A Theoretical Interpretation of the Achievements of Shenzhen's Modernization from 1978 [J]. Shenzhen Social Sciences, 2019(01): 97-110+159