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Similarity Search in Time Series Databases based on the Normalized Distance  

이상준 (서울대학교 전기컴퓨터공학부)
이석호 (서울대학교 전기컴퓨터공학부)
Publication Information
Journal of KIISE:Databases / v.31, no.1, 2004 , pp. 23-29 More about this Journal
Abstract
In this paper, we propose a search method for time sequences which supports the normalized distance as a similarity measure. In many applications where the shape of the time sequence is a major consideration, the normalized distance is a more suitable similarity measure than the simple Lp distance. To support normalized distance queries, most of the previous work has the preprocessing step for vertical shifting which normalizes each sequence by its mean. The proposed method is motivated by the property of sequence for feature extraction. That is, the variation between two adjacent elements of a time sequence is invariant under vertical shifting. The extracted feature is indexed by the spatial access method such as R-tree. The proposed method can match time series of similar shape without vertical shifting and guarantees no false dismissals. The experiments are performed on real data(stock price movement) to verify the performance of the proposed method.
Keywords
database; time series; similarity search; normalized distance;
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