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发表时间：2022-10-10

发表刊物：西南师范大学学报 自然科学版

期号：11

页面范围：130-136

ISSN号：1000-5471

摘要：Characteristics of continuous ,massive and rapid make the traditional imputation algorithm can not be applied to data stream .In this paper ,a nested sliding window‐based missing data imputing algo‐rithm has been proposed .Taking into account the aging characteristics of the data stream of sensor net‐works ,we use a nested sliding window to select the data ,both of which have high spatial correlation and nearest data ,as sample data ,then to impute the missing data by two cases .Firstly ,we use the Pearson correlation to analysis the spatial relation of data ,then use nested sliding window to select the sample data which have strong spatial relation to each others ,then use MKNN algorithm to accurate impute .Pearson correlation analysis and nested window greatly reduced the data size greatly ,improved the real‐time pro‐cessing ;For missing data w hich do not having strong spatial correlation ,using simple linear correlation al‐gorithm to impute to reduce the complexity .Experimental results show that this algorithm can accurately to impute the missing data of data flow in real time .

备注：新增回溯数据