NDLI: Incremental methods for simple problems in time series: algorithms and experiments

Content Provider	IEEE Xplore Digital Library
Author	Xiaojian Zhao Xin Zhang Neylon, T. Shasha, D.
Copyright Year	2005
Description	Author affiliation: Courant Inst. of Math. Sci., New York Univ., NY, USA (Xiaojian Zhao; Xin Zhang; Neylon, T.; Shasha, D.)
Abstract	A time series (or equivalently a data stream) consists of data arriving in time order. Single or multiple data streams arise in fields including physics, finance, medicine, and music, to name a few. Often the data comes from sensors (in physics and medicine for example) whose data rates continue to improve dramatically as sensor technology improves and as the number of sensors increases. So fast algorithms become ever more critical in order to distill knowledge from the data. This paper presents our recent work regarding the incremental computation of various primitives: windowed correlation, matching pursuit, sparse null space discovery and elastic burst detection. The incremental idea reflects the fact that recent data is more important than older data. Our StatStream system contains an implementation of these algorithms, permitting us to do empirical studies on both simulated and real data.
Starting Page	3
Ending Page	14
File Size	304144
Page Count	12
File Format	PDF
ISBN	0769524044
ISSN	10988068
DOI	10.1109/IDEAS.2005.35
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Matching pursuit algorithms Physics Null space Earth Filters Finance Space technology Computational modeling Satellites Data security
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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