Similarity Search of Time Series with Moving Average Based Indexing
用基于移动均值的索引实现时间序列相似查询

In this paper,a method called MABI(moving average based indexing)is proposed to effectively deal with the issue of c-search query in subsequence matching.Two important theorems,distance reduction theorem and DRR(distance reduction rate)relation theorem,are proposed here to be as the basis of MABI.DRR relation theorem has strong capability in"pruning"those unqualified candidate sequences so as to achieve of fast similarity search. Furthermore,by modifying BATON~* introduced by Jagadish,et al.,a multi-way balanced tree structure is introduced,to construct the index from time series,which significantly speeds up the similarity search.Extensive experiments over a stock exchange dataset show that MABI can achieve desirable performance.