摘要
针对时空数据异常识别精度不足的问题,从时间维度和空间维度融合思想出发,构建了一个时空数据异常识别框架.基于该框架,在分布未知情况下,采用阿基米德Copula函数推导了不同空间位置属性数据之间的差异概率.与此同时,建立了以高值点数为核心的空间数据转化方法,形成了空间秩序列,确定了用于假设检验的期望和方差.最后,以窗口大小、范围半径为模型参数,通过M-K检验给出了异常数据的识别方法.实验表明,该方法能够进一步提高时空数据异常检测精度,具有更强的识别能力.
Aiming at the problem of low accuracy of outlier recognition for spatio-temporal data,a framework was constructed according to fusion thought of time dimension and space dimension.Based on the framework,the difference of attribute data between different positions was derived by Archimedean copulas function under the condition of unknown distribution.A method for converting spatial data was established with high value as a core to build rank series.Then,the expectation and variance were determined for hypothesis test.Finally,with the model parameters of window size and scope radius,an approach of outlier recognition for spatio-temporal data was given based-on M-K test.The calculation example and application analysis show that this approach can improve the accuracy of outlier recognition for spatio-temporal data,and has more recognition capability.
作者
李建勋
张锐军
SAFONOV Paul
佟瑞
LI Jianxun;ZHANG Ruijun;SAFONOV Paul;TONG Rui(School of Economics and Management,Xi'an University of Technology,Xi'an 710048,China;Herberger Business School,Saint Cloud State University,Saint Cloud 56301,USA)
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2019年第12期3229-3236,共8页
Systems Engineering-Theory & Practice
基金
“十二五”国家水体污染控制与治理重大专项课题(2012ZX07201-006)
陕西省社会科学基金(2018S08)
陕西省教育厅专项科研计划项目(18JK0577)~~
