Extended symbolic aggregate approximation based anomaly mining of hydrological time series
基于扩展符号聚集近似的水文时间序列异常挖掘

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.