NDLI: A geometrical solution to time series searching invariant to shifting and scaling

Content Provider	Springer Nature Link
Author	Zhou, Mi Wong, Man Hon Chu, Kam Wing
Copyright Year	2005
Abstract	The technique of searching for similar patterns among time series data is very useful in many applications. The problem becomes difficult when shifting and scaling are considered. We find that we can treat the problem geometrically and the major contribution of this paper is that a uniform geometrical model that can analyze the existing related methods is proposed. Based on the analysis, we conclude that the angle between two vectors after the Shift-Eliminated Transformation is a more intrinsical similarity measure invariant to shifting and scaling. We then enhance the original conical index to adapt to the geometrical properties of the problem and compare its performance with that of sequential search and R-tree. Experimental results show that the enhanced conical index achieves larger improvement on R-tree and sequential search in high dimension. It can also keep a steady performance as the selectivity increases.
Starting Page	202
Ending Page	229
Page Count	28
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	9
Issue Number	2
e-ISSN	02193116
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-05-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Information search and retrieval Similarity search Spatial indexing Time series database Business Information Systems Information Systems and Communication Service
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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