It is now one of the hot spots in scientific research to use data mining technology over long-term observations of timeseries to find the regular pattern. Similarity mining is the hasis for other tasks of time series data mining. This paper proposed a semantic similarity-based similarity search method over hydrological time-series data. Firstly, wavelet transform was employed to smooth the time-series data; then the extreme points were extracted and the segments were symbolized. each of which represents a semantic pattern. The semantically similar sequences were as the candidate set, so as to get the similar suh-sequences precisely from the candidate set by using dynamic: time warping. Experiments on the water level of Taihu Lake show that the method can find the similar sequences accurately while the time complexity is significantly reduced.%利用数据挖掘技术从长期观测的数据序列中发现蕴藏的规律是当前研究热点之一.相似性挖掘是时间序列挖掘的基础,提出一种基于语义相似的水文时间序列相似性查询方法.首先利用小波变换将时间序列进行平滑处理,在此基础上进行极值点分段并符号化,每个符号代表一种语义模式,从而选取语义相似的子序列作为候选集,再将候选集中子序列通过动态时间弯曲距离进行精确匹配从而得到相似子序列(以太湖流域大浦口站水位数据为例),实验证明,该方法能够在大幅度降低时间复杂度的基础上较准确地查找出相似子序列.
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