﻿ 基于时间序列的麻疹发病率分析 Analysis of Measles Incidence Based on Time Series

Statistics and Application
Vol.06 No.05(2017), Article ID:23239,6 pages
10.12677/SA.2017.65062

Analysis of Measles Incidence Based on Time Series

Dan Zhou

School of Mathematics & Physics Science and Engineering, Anhui University of Technology, Maanshan Anhui

Received: Dec. 6th, 2017; accepted: Dec. 22nd, 2017; published: Dec. 29th, 2017

ABSTRACT

Based on the national measles morbidity data from 1950 to 2014, we establish a model to effectively predict the incidence of measles in the short term through the time series analysis method, so as to issue warnings on the epidemic situation of measles and prepare the preventive work in advance.

Keywords:Time Series, Measles, Exponential Smoothing

1. 引言

2. 主要内容

2.1. 背景

2.2. 数据来源

Figure 1. Morbidity time series chart

Figure 2. Mortality time series chart

2.3. 模型

20世纪60年代，George Box与Gwilym Jenkins提出了一种关于时间序列分析、预测的方法，称之为B-J模型，也叫做ARMA (Auto Regression Moving Average)模型。ARMA模型 [5] 的基本模型有三种情况：自回归模型(AR模型)；滑动平均模型(MA模型)；自回归滑动平均模型(ARMA模型) [6] 。

${\stackrel{^}{X}}_{t+1}=\alpha {X}_{t}+\alpha \left(1-\alpha \right){\stackrel{^}{X}}_{t}$

${\stackrel{^}{X}}_{t+1}=\alpha {X}_{t}+\alpha \left(1-\alpha \right){X}_{t-1}+\alpha {\left(1-\alpha \right)}^{2}{X}_{t-2}+\cdots +\alpha {\left(1-\alpha \right)}^{t-1}{X}_{1}+{\left(1-\alpha \right)}^{t}{\stackrel{^}{X}}_{1}$

2.4. 结论

Table 1. Model statistics

Table 2. Exponential smoothing model parameters

Table 3. Predictors and actuals

Figure 3. Observation charts

Figure 4. Measles incidence map around

Analysis of Measles Incidence Based on Time Series[J]. 统计学与应用, 2017, 06(05): 550-555. http://dx.doi.org/10.12677/SA.2017.65062

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