ARIMA乘积季节模型与广义回归神经网络模型在布鲁菌病发病预测的比较

doi:10.6040/j.issn.1671-7554.0.2017.1185

摘要/Abstract

摘要： 目的探讨适合全国布鲁菌病发病的预测模型,为布鲁菌病预测预警系统提供参考。方法利用中国疾病预防控制中心2011年1月至2016年12月按月报告的布鲁菌病发病数历史疫情数据,分别建立求和(差分)自回归移动平均(ARIMA)乘积季节预测模型和广义回归神经网络(GRNN)模型,对2017年1~8月月报数进行预测,采用实际发病数与两种模型预测数进行比较,评价指标为平均相对误差、平均绝对误差。结果建立的ARIMA(0,1,1)(0,1,1)₁₂乘积季节模型平均绝对误差、平均相对误差分别是989、0.23,GRNN模型平均绝对误差、平均相对误差分别是561、0.14,均小于ARIMA模型。结论 ARIMA模型和GRNN模型均可用于预测布病的发病数,后者预测效能优于前者。

关键词: 求和(差分)自回归移动平均, 乘积季节模型, 布鲁菌病, 广义回归神经网络模型, 时间序列, 预测

Abstract: Objective To explore suitable model for brucellosis incidence forecasting in China, and to provide reference for forecasting warning system of brucellosis. Methods Autoregressive integrated moving average(ARIMA)model and generalized regression neural network(GRNN)model were fitted with data monthly reported by China Centers for Disease Control from January 2011 to December 2016. The monthly reported data from January to August 2017 were used to evaluate forecast results. The mean absolute error(MAE)and mean relative error(MRE)were evaluated by comparing the actual incidence with the predicted incidence of the two models. Results The MAE and MRE of the ARIMA(0,1,1)(0,1,1)₁₂model were 989, 0.23 and the GRNN model were 561, 0.14, respectively. The MAE and MRE of the GRNN model were less than the ARIMA model. Conclusion Both the ARIMA and GRNN model perform well in forecasting the incidence of brucellosis, while the prediction ability of GRNN model is slightly better than ARIMA model.

Key words: Autoregressive integrated moving average, Multiple seasonal model, Brucellosis, Generalized regression neural network model, Time series, Forecast

中图分类号:

R183

马洁,田野,黄璐,孟维静,王素珍,石福艳. ARIMA乘积季节模型与广义回归神经网络模型在布鲁菌病发病预测的比较[J]. 山东大学学报 (医学版), 2018, 56(6): 76-82.

MA Jie, TIAN Ye, HUANG Lu, MENG Weijing, WANG Suzhen, SHI Fuyan. Comparison of multiple seasonal ARIMA model and generalized regression neural network model in forecasting the incidence of brucellosis[J]. Journal of Shandong University (Health Sciences), 2018, 56(6): 76-82.

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