A Combined Algorithm for High-dimensional Similarity Search in Time Series Database

In this paper, a WSTB-based algorithm for high-dimensional similarity search is proposed based on the float searching algorithm which is used to select features. The float searching algorithm to reduce the dimension of time series is used to get the piecewise linear features of the sample. When the subsequence bin for time series and the index of the bin is built, the sample series is calculated with the similarity distance. Quick index can be realized without checking the content of the bin, because the calculation gotten from comparison one by one is avoided. At last, the currency and efficiency of the algorithm are proved.