%0 Journal Article
%T Extended symbolic aggregate approximation based anomaly mining of hydrological time series<br>基于扩展符号聚集近似的水文时间序列异常挖掘
%A LIU Qian
%A ZHU Yue-long
%A ZHANG Peng-cheng
%A <br>刘　千
%A 朱跃龙
%A 张鹏程
%J 计算机应用研究
%D 2012
%I 
%X Most of anomaly mining of hydrological time series uses distance based method. Since the method was time-consuming and has a great deal of computation, this paper applied extended symbolic aggregate approximation and then measured distance of strings. It verified the validity of the method by the water level data obtained from Xiaomeikou gauge station in the Taihu Lake. The experimental results show that the method has high efficiency and is more suitable for processing large-scale data sets.
%K hydrological time series
%K anomaly mining
%K symbolization
%K distance measure<br>水文时间序列
%K 异常挖掘
%K 符号化
%K 距离度量
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=8D8174D12B4331BFCD2D24A2A4600BB9&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=59906B3B2830C2C5&sid=F4ED3FC619E88D15&eid=8B6C9E81E85AE4B4&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=12