水文时间序列变点识别的一种稳健方法

针对目前水文时间序列变点识别研究中忽略了方法的稳健性，未能充分考虑异常值的影响的不足，提出了利用一种高度稳健的高斯混合密度分解算法来识别水文时间序列中的变点，并以此来研究水文时间序列均值的突变该方法的核心是根据观测到的资料，通过逐步挖掘服从不同正态分布的时间序列分支，将均值变点的识别问题转化为混合正态密度的聚类问题，从而达到估计变点的位置以及自动获得变点的数目估计的目的实例计算结果表明，该方法对含有噪声的时间序列数据，仍能准确识别变点的位置，较好地解决了水文序列变点识别的稳健性问题．

A Robust Method for Change-Points Detection in Hydrological Time Series

Traditional studies on the detection of change-points in hydrological time series considering little infection of noise always ignore the robust of the methods.In this paper,a highly robust Gaussian mixture density decomposition method is proposed for finding change-point of the mean value for hydrological time series.Given the observed hydrological data,different parts of the time series which are realized from different normal distributions are identified successively.Thus identifying change-point is tran...