基于STL-ADABOOST-ESN组合模型在全国HIV月发病数预测中的应用

秦览; 陈继军; 于国伟

doi:10.16462/j.cnki.zhjbkz.2020.01.016

基于STL-ADABOOST-ESN组合模型在全国HIV月发病数预测中的应用

doi: 10.16462/j.cnki.zhjbkz.2020.01.016

1.
730000 兰州, 兰州大学数学与统计学院
2.
730000 兰州, 兰州市疾病预防控制中心
3.
730000 兰州, 西北民族大学西部环境健康研究所

基金项目: 兰州市人才创新创业项目(2017-RC-14)

详细信息

通讯作者:
陈继军, E-mail:chenjijun0425@126.com

中图分类号: R512.91
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- 被引次数: 0
出版历程
- 收稿日期: 2019-06-28
- 修回日期: 2019-09-28
- 刊出日期: 2020-01-10

Using the hybrid model STL-ADABOOST-ESN for forecasting the monthly number of HIV patient in China

1.
School of Mathematics and Statistic, Lanzhou University, Lanzhou 730000, China
2.
The Lanzhou Center for Disease Control and Prevention, Lanzhou 730000, China
3.
West Institute of Environment and Health, Northwest Minzu University, Lanzhou 730000, China

Funds: Lanzhou Talent Innovation and Entrepreneurship Project(2017-RC-14)

More Information

Corresponding author: CHEN Ji-jun, E-mail:chenjijun0425@126.com

摘要

摘要: 目的根据全国人类免疫缺陷病毒(human immunodeficiency virus, HIV)月发病数的情况, 采用基于Loess季节趋势分解方法(seasonal-trend decomposition procedures based on loess, STL)和自适应提升(adaptive boosting, AdaBoost)框架下的回声状态网络(echo state network, ESN), 构建HIV月发病数模型, 并预测2017年全国HIV月发病数。方法从中国疾病预防控制中心官网收集2013年1月-2016年12月全国HIV月发病数, 通过STL将月发病数序列分解为季节和非季节序列, 对两部分分别采用简单的季节估计方法和ADABOOST-ESN进行建模, 最后将它们的输出值进行加和, 得到HIV月发病数的预测值。结果本文以均方根误差(root mean squared error, RMSE)和平均绝对百分比误差(mean absolute percentage error, MAPE)作为模型性能的评价指标, 得到STL-ADABOOST-ESN的建模性能RMSE和MAPE分别为164.083和1.842%, 预测性能RMSE和MAPE分别为359.404和3.776%, 其预测精度高于乘积季节模型(seasonal autoregressive integrated moving average, SARIMA)、ESN、ADABOOST-ESN和STL-ESN等模型。采用本文提出的方法得出2017年1-12月全国HIV发病数预测值为5 654~8 497人。结论本研究提出的STL-ADABOOST-ESN模型预测准确度较高, 预测2017年全国HIV年发病数将达到92 040人, 相比于2016年增长了4.87%, 因此有必要在全国实施更为严格的HIV预防控制工作。
- HIV /
- 发病数预测 /
- 季节调整方法 /
- 状态回声网络 /
- 集成框架
Abstract: Objective Based on the monthly number of human immunodeficiency virus(HIV) patient in China, this paper utilizes seasonal-trend decomposition procedure based on loess(STL) and echo state network(ESN) under the integrated framework adaptive boosting(AdaBoost) to build a hybrid model, and uses it to forecast the epidemic situation in 2017. Methods From the official website of center of Chinese disease prevention and control, we collected the data concerning the monthly number of HIV infection in China from January 2013 to December 2016. Firstly, the time series was decomposed into seasonal and remainder components via STL. Then, the simple seasonal estimation method was used to model the seasonal component, and ADABOOST-ESN was employed to model the remainder component. Finally, the output values of two components are summed to obtain the forecasting results of the monthly number of HIV infection. Results The assessment indices including root mean square error(RMSE) and mean absolute percentage error(MAPE), were used to evaluate the performance of model. The results showed that the values of RMSE and MAPE given by STL-ADABOOST-ESN were 164.083 and 1.842% respectively in training set, 359.404 and 3.776% respectively in test set. In addition, the forecasting accuracy of STL-ADABOOST-ESN was absolutely higher than other four models, including seasonal autoregressive integrated moving average(SARIMA), ESN, ADABOOST-ESN, STL-ESN. The monthly number of HIV patient in 2017 was predicted between 5 654 and 8 497 via the proposed method. Conclusions The model STL-ADABOOST-ESN proposed by this paper has high prediction accuracy. The forecasting results showed that the annual number of HIV patient in 2017 will reach 92 040, which increased by 4.87 percentage compared to 2016. Therefore, it is imperative to implement stricter measures of HIV prevention and control in China.
- HIV /
- Prediction for the number of the infected /
- Seasonal adjustment method /
- Echo state network /
- Integration framework

HTML全文

图 1 2013-2016年全国HIV月发病数分布

Figure 1. The distribution of monthly incidence of HIV in China from 2013 to 2016

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图 2 ESN的基本结构

Figure 2. The basic structure of ESN

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图 3 模型ADABOOST-ESN的流程图

Figure 3. The flowchart of ADABOOST-ESN model

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图 4 混合模型STL-ADABOOST-ESN流程图

Figure 4. The flowchart of hybrid model STL-ADABOOST-ESN

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图 5 原始序列和分解得到的子序列

Figure 5. The original time series and decomposed subseries

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图 6 2016年真实数据与2017年预测数据的比较

Figure 6. 6 The comparison between actual data in 2016 and predictive data in 2017

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表 1 不同模型预测精度比较

Table 1. Comparison of forecasting accuracy of different models

模型	训练集		测试集
模型	RMSE	MAPE (%)	RMSE	MAPE (%)
SARIMA	690.963	7.898	673.846	7.497
ESN	285.309	3.887	552.133	6.638
ADABOOST-ESN	238.729	3.021	530.963	6.413
STL-ESN	206.697	2.424	391.330	3.995
STL-ADABOOST-ESN	164.083	1.842	359.404	3.776

下载: 导出CSV

表 2 2016年HIV月发病数预测结果

Table 2. The forecasting results of the number of HIV infection in 2016

月份(月)	实际值	SARIMA	ESN	ADABOOST-ESN	STL-ESN	STL-ADABOOST-ESN
1	6 270	6 402	7 284	7 005	6 534	6 523
2	4 631	4 186	4 288	4 812	4 629	4 514
3	8 221	7 057	8 104	7 813	7 853	7 990
4	7 422	7 048	7 825	7 762	6 851	7 015
5	7 239	6 842	6 982	6 852	7 274	7 144
6	7 499	7 077	8 220	7 842	7 146	7 375
7	7 701	7 861	8 136	8 237	7 509	7 512
8	8 119	6 848	7 758	7 135	7 253	7 249
9	8 102	7 341	8 071	8 144	8 191	8 165
10	6 541	6 129	5 920	5 965	6 196	6 038
11	7 594	6 594	7 332	7 240	7 646	7 662
12	8 425	8 311	9 398	9 163	7 911	8 007

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