一种基于DTW聚类的水文时间序列相似性挖掘方法

DTW Clustering-based Similarity Mining Method for Hydrological Time Series

水文时间序列相似性挖掘是水文时间序列挖掘的重要方面,对洪水预报、防洪调度等具有重要意义。针对水文数据的特点,提出了一种基于DTW聚类的水文时间序列相似性挖掘方法。该方法先对数据进行小波去噪、特征点分段以及语义划分,再基于DTW距离对划分后的子序列做层次聚类并符号化;然后根据符号序列间的编辑距离筛选候选集;最后通过序列间的DTW距离进行精确匹配,获取相似水文时间序列。以滁河六合站的日水位数据进行实验,结果表明,所提方法能够有效地缩小候选集,提高查找语义相似的水文时间序列的效率。

Similarity mining of hydrological time series is an importance aspect of hydrological time series mining.It will be of great importance in flood forecasting and flood control scheduling.According to the characteristics of hydrological data,this paper proposed a DTW clustering-based similarity mining method over hydrological time series.Firstly,on the premise of wavelet denoising,feature point segmentation and semantic classification,hierarchical cluster analysis is used to the classified sub-sequences based on DTW distance and the sub-sequences are symbolized.Then,candidate sets of time series are filtered according to the edit distance between symbol sequences.Finally,the similar hydrological time series are got precisely from the candidate sets by DTW exact matching.Experiments on the water level of Chuhe Liuhe station show that the proposed method can narrow the candidate sets effectively and improve the efficiency of searching for semantic similarity of hydrological time series.