基于Copula函数和M-K检验的时空数据异常识别方法.

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. [ABSTRACT FROM AUTHOR]