LIU Tian1,2, ZHANG Li-jie2, WENG Xi-jun2, MA Hui-lai2, YAO Meng-lei1, HUANG Ji-gui1, WU Yang3
1.Jingzhou Municipal Center for Disease Control and Prevention, Jingzhou, Hubei 434000, China; 2.Chinese Field Epidemiology Training Program, Beijing 100050, China; 3.Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei 430079, China
Abstract：Objective To compare the fitting and prediction effects of SARIMA models with different parameter settings, and to provide a basis for improving the accuracy of SARIMA model. Methods The monthly incidence rates of hand, foot and mouth disease (HFMD) in China from January 2009 to June 2015 were collected. The parameters p and q were determined according to the conventional graphic method, and the SARIMA model was established and recorded as the model 1. Then the parameters p and q were added or decremented by 1, a plurality of candidate models were constructed, and the optimal model was filtered and recorded as the model 2. The model 1 and the model 2 were employed to predict the incidence rates of HFMD from July to December in 2015, and then compared them with the actual values. Mean absolute percentage error (MAPE), mean error rate (MER), mean square error (MSE), and mean absolute error (MAE) were used to evaluate the fitting and prediction effects of the two models. Results Model 1 was SARIMA(1,0,0)(1,1,0)12. There were two models 2, including SARIMA(1,0,1)(1,1,0)12 and SARIMA(1,0, 1) (0, 1, 1)12. In the fitting phase, the MAPE fitted by the SARIMA(1,0,0)(1,1,0)12, SARIMA(1,0,1)(1,1,0)12 and SARIMA(1,0, 1)(0, 1, 1)12 were 22.891%, 20.015% and 19.985%, respectively. The MAPE, MER, MSE and MAE predicted by the SARIMA(1,0,0)(1,1,0)12, SARIMA(1,0,1)(1,1,0)12 and SARIMA(1,0, 1) (0, 1, 1)12 were 9.119%, 8.988%, 1.874 and 1.107; 11.000%, 10.909%, 2.552 and 1.344; 8.711%, 8.477%, 1.857 and 1.044, respectively. Conclusions SARIMA (1, 0, 1)(0, 1, 1)12 is the best model, and its fitting and prediction effects are better than those of the SARIMA(1,0,0)(1,1,0)12 model established by the graphic method. In the SARIMA modeling process, the approximate range of model parameters should be determined by the graphic method, and the optimal parameters should be screened by step-by-step methods to improve the accuracy of the model.
[1] 李少雄,李本光.基于SARIMA模型和X-12-ARIMA季节调整方法预测的比较[J].统计与决策,2018,34(18):39-42. [2] 王盼盼,郭林豪,王林枝,等.SARIMA模型在湖泊水温序列中的应用[J].河北渔业,2017(8):21-23,34. [3] Bozkurtöö, Biricik G, Taysi Z C. Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market[J]. PLoS One, 2017, 12(4):e0175915. [4] Wang YW, Shen ZZ, Jiang Y. Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China[J]. PLoS One, 2018,13(9):e201987. [5] Wang Y, Xu C, Zhang S, et al. Temporal trends analysis of human brucellosis incidence in mainland China from 2004 to 2018[J]. Sci Rep, 2018,8(1):15901. [6] 王燕. 应用时间序列分析-第2版[M]. 北京:中国人民大学出版社, 2008:65-68. [7] 孙振球. 医学统计学.第3版[M]. 北京:人民卫生出版社, 2010. [8] 时照华. 探索ARIMA模型在呼吸道传染病疫情预测中的应用[D].合肥:安徽医科大学, 2013:22-23. [9] Xu Q, Li R, Liu Y, et al. Forecasting the incidence of mumps in Zibo city based on a SARIMA model[J]. Int J Environ Res Public Health, 2017, 14(8):E925. [10] 张伟文,贺湘焱,古丽娜扎尔·艾克拜尔,等.季节时间序列分析在新疆地区涂阳结核疫情预测预警中的应用[J].实用预防医学,2019,26(1):26-29. [11] 周美兰,周志华,罗美玲,等.湖南省哨点医院流感样病例SARIMA模型预测[J].实用预防医学,2018,25(3):370-373. [12] Li S, Cao W, Ren H, et al. Time series analysis of hemorrhagic fever with renal syndrome: a case study in Jiaonan County, China[J]. PLoS One, 2016, 11(10):e0163771. [13] 李杰, 顾月. ARIMA模型在预测手足口病发病中的应用[J]. 预防医学, 2016, 28(10):987-991. [14] 沈忠周, 马帅, 曲翌敏, 等. ARIMA模型在我国法定传染病报告数中的应用[J]. 中华流行病学杂志, 2017, 38(12):1708-1712.
2019, Vol. 26 
