Minimum distance queries for time series data

Abstract

In this paper, we propose an indexing scheme for time sequences which supports the minimum distance of arbitrary L p norms as a similarity measurement. In many applications where the shape of the time sequence is a major consideration, the minimum distance is a more suitable similarity measurement than the simple L p norm. To support minimum distance queries, most of the previous work has the preprocessing step for vertical shifting which normalizes each sequence by its mean. The vertical shifting, however, has the additional overhead to get the mean of a sequence and to subtract it from each element of the sequence. The proposed method can match time series of similar shape without vertical shifting and guarantees no false dismissals. In addition, the proposed method needs only one index structure to support minimum distance queries in any arbitrary L p norm. The experiments are performed on real data (stock price movement) to verify the performance of the proposed method.