Similarity Search of Time Series with Moving Average Based Indexing
提出了基于移动均值的索引来解决子序列匹配中的"(-查询"问题;提出并证明了基于移动均值的缩距定理和缩距比关系定理,后者具有很好的"裁减"能力,可以在相似查询时淘汰大部分不符合条件的候选时间序列,从而达到快速相似查找的目的;引入了由Jagadish 等人提出的BATON*-树,并在此基础上适当修改,建立了MABI索引,极大地加快了相似查询过程;最后,在一个股票交易数据集上进行了实验,证明了MABI索引的良好性能.In this paper, a method called MABI (moving average based indexing) is proposed to effectively deal with the issue of (-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.