随机缺失数据下的时间序列分析建模 |
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引用本文: | 胡隽,曹显兵.随机缺失数据下的时间序列分析建模[J].数学的实践与认识,2014(20). |
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作者姓名: | 胡隽 曹显兵 |
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作者单位: | 北京工商大学理学院; |
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基金项目: | 北京市高校创新人才项目(201106206) |
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摘 要: | 在时间序列建模过程中,数据的缺失会极大地影响模型的准确性,因此对缺失数据的填补尤为重要.选取北京市空气质量指数(AQI)数据。将其随机缺失10%.分别利用EM算法和polyfit直线拟合的方法对缺失值插补,补全数据后建立ARMA模型并作预测分析.结果表明,利用polyfit函数插补法具有较好的结果.
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关 键 词: | 缺失数据 EM算法 polyfit函数 ARMA模型 Matlab |
Time Series Modeling for Missing Data at Random |
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Abstract: | It is well known that the accuracy of a time series model greatly depends on the collected data,and therefore it is very important to handle the data with missing values.In this paper,based on the data of Beijing Air Quality(AQI),10%of it is missing at random,the ARMA models are estabhshed by imputation method of missing data based on both EM algorithm and polyfit line-fitting algorithm.The results show that the polyfit algorithm is more effective. |
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Keywords: | missing value EM algorithm polyfit function ARMA model Matlab |
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