多元水文时间序列相似性挖掘的研究与应用

The research and application of multivariate time series similarity data mining

利用数据挖掘技术从长期观测的数据中发现蕴藏的规律是当前研究的热点之一。相似性挖掘是时间序列挖掘的基础,文章提出了一种新的基于改进的BORDA计数的多元时间序列相似性查询方法。首先利用PCA对多元时间序列进行降元并获取每元主成分的方差贡献率作为权值,然后分别计算单序列的相似性,利用BORDA计数法分别积分,以BORDA得分乘以权值综合得到最终得分来衡量相似性。文章以宜丰洪水时间序列相似性研究为例,验证了提出方法的可行性和有效性。

How to discover the hidden knowledge among data collected by various sensors during the last years has caused more and more attention.Similarity mining is the basic of time series data mining.This paper deals with similarity mining from hydrological time series and concentrates itself on the similarity analysis of multivariate time series（MTS）.A novel similarity measure has been put forward,which is based on a improved BORDA count in multiple classifier system.Firstly,dimension reduction is adaptively conducted according to the target data complexity in PCA and the contribution rate of the variance,then the similarity of single time series is computed and lastly,the overall similarity of the MTS is obtained by synthesize each of the single similarity based on the improved BORDA count.Experiments on the similarity analysis of historical flood data from Yifeng basin have shown the feasibility and effectiveness of the proposed method.