In order to explore the evolution law and future state characteristics of China's tertiary industry contribution rate, this paper selects the contribution index data of China's tertiary industry to GDP from 1990 to 2020. It uses the ARIMA model to predict the future data of China's tertiary industry contribution index to GDP from 2021 to 2022. A stationary time series data is obtained through the stationary processing of time series, and finally, a prediction model of ARIMA (1,1,3) is established. The results show that the contribution index of China's tertiary industry to GDP shows a cyclical fluctuation of increase and decrease. The rising trend indicates that China's economic restructuring has achieved specific results. The validity of the predicted data is verified by using the actual tertiary industry contribution index data of GDP in 2020, proving the rationality of the predicted results. Furthermore, the predicted data in 2022 show a downward trend. On this basis, relevant suggestions are put forward to develop the tertiary industry vigorously and steadily promote the transformation and upgrading of economic structure.

Chenfei Du, Shuzhi Yang, Xinyi Song. Analysis and Prediction of the Contribution Rate of China's Tertiary Industry Based on ARIMA Model. Academic Journal of Business & Management (2023) Vol. 5, Issue 13: 153-160. https://doi.org/10.25236/AJBM.2023.051322.

[1] Feng Qiong, Bi Zhongfei, Liu Jun. Pressure of agricultural water resources in the huai river region space-time evolution and forecast analysis [J]. Water and electricity energy science, 2022, 40 (11): 27 to 30, DOI:10.20040/j.carolcarrollnki.1000-7709. 2022. 20220023. 

[2] Yao Jinhai, Zou Jiajun. CPI SVM prediction - ARIMA model establishment and numerical simulation [J]. Journal of statistics and decision, 2022, 38 (21): 48-52. DOI: 10. 13546 / j. carol carroll nki tjyjc. 2022. 21. 009. 

[3] Yao J H. Research on Stock index prediction based on ARIMA and Information granulated SVR [J]. Operations Research and Management, 2002, 31(05): 214-220. 

[4] Wang Xiangqian, Wu Donglong, Zheng Jiantong. Improved ARIMAX Method for Cargo Throughput Prediction: A case study of Tianjin Port [J]. Operations Research and Management, 2002, 31(03): 138-144. 

[5] Zhou Xiaoliang, Feng Xu, Yan Maolin, Zhang Yang, Wu Chengliang, Liu Jinglan. A Study on the Future Supply Strategy of Woody Oils in China Based on ARIMA Model [J]. China Oils and Fats, 2022, 47 (03): 94-99. DOI: 10. 19902/j. cnki. zgyz. 1003-7969. 210201

[6] Lun Runqi, Luo Qiyou, Gao Mingjie, Yang Yadong. Prediction of potato price in China based on combination model [J]. Chinese Journal of Agricultural Resources and Regional Planning, 2021, 42(11): 97-108. 

[7] Xie Xianfen, Wang Binhui, Gu Wanrong, Yang Ying. Construction of Innovation Environment Index and Research on Dynamic Early Warning and Monitoring Based on Adaptive ARIMA [J]. Forum on Statistics and Information, 2021, 36(06): 3-13. 

[8] Liu Jinpei, Wang Piao, Huang Yanyan, Tao Zhifu. Based on interval time series wavelet multi-scale decomposition of combination forecast method [J]. Journal of statistics and decision, 2020, 4 (19): 5-9. DOI: 10. 13546/j. carol carroll nki tjyjc. 2020. 19. 001. 

[9] Cai Chengzhi, Jiang Xingzi, Liang Ying. Analysis of world wheat supply and demand situation based on ARIMA model [J]. Agricultural Economics, 2020(09): 112-114. 

[10] Chen Yanming, Bai Ziyu, Liu Dan. Chinese exports plastic products based on ARIMA model prediction and analysis [J]. Journal of plastic science and technology, 2020 (8): 13. 134-137 DOI: 10. 15925 / j. carol carroll nki issn1005-3360. 2020. 08. 031. 

[11] Tan Z L, Yuan H. Research on the relationship between time series preprocessing and information noise: based on discrete wavelet transform and ARIMA model [J]. Practice and Understanding of Mathematics, 2020, 50(15): 30-42. 

[12] Xie Chengxing, Wang Fengxiao. GDP forecasting of Kashgar Region based on ARIMA-DGM-BP combination model [J]. Practice and Understanding of Mathematics, 2020, 50(15): 43-48. 

[13] Peng Shiguang, Geng Xianhui. Prediction of China's soybean import volume and import value based on ARIMA and GM (1, 1) model [J]. Soybean Science, 2020, 39(04): 626-632. 

[14] Lin Xuefei, Wang Baohai. Analysis and Prediction of Beef Price in China Based on HP Filter Decomposition ARIMA-GARCH Model [J]. Heilongjiang animal husbandry and veterinary, 2020 (12): 30-34 + 158. DOI: 10. 13881 / j. carol carroll nki hljxmsy. 2019. 05. 0199. 

[15] Yang Jinwei, Li Yimao. Prediction of Iron Ore Price Index in China based on ARIMA Model [J]. Mathematics in Practice and Understanding, 2020, 50(11): 289-298. 

[16] Peng Hongxing, Zheng Kaihang, Huang Guobin, Lin Du-sheng, Yang Zhi-chao, Liu Hua-nai. Vegetable prices based on BP, LSTM and ARIMA model prediction [J]. Journal of Chinese agricultural mechanization, 2020, 9 (4): 193-199. The DOI: 10. 13733 / j. j CAM. Issn 2095-5553. 2020. 04. 031. 

[17] Shi Y. Research on the production prediction of plastic products in China based on ARIMA time series model [J]. Plastic science and technology, 2020, 13 (3): 115-118. DOI: 10. 15925 / j. carol carroll nki issn1005-3360. 2020. 03. 028. 

[18] Yang Yufang, Zhao Huifeng. Based on the ARIMA model fluctuation analysis and prediction of beef cattle production in hebei province [J]. Journal of animal husbandry and veterinary in heilongjiang province, 2020 (6): 16-19. DOI: 10. 13881 / j. carol carroll nki hljxmsy. 2019. 03. 0011. 

[19] Zhang Yifan, Fan Meihua. Analysis and Prediction of egg Price in National Market Based on ARIMA Model [J]. And poultry in China, 2020 (01): 82-86. The DOI: 10. 16372 / j. i SSN. 1004-6364. 2020. 01. 017. 

[20] Luo Zhidan, Liu Ying, Guo Wei. Short-term Prediction of Stock Price Based on Taylor Expansion and ARIMA Hybrid Model Based on Tracking Differentiator [J]. Mathematics in Practice and Understanding, 2019, 49(23): 67-77.