Database Reference
In-Depth Information
Chapter 5
DIMENSIONALITY REDUCTION AND
FILTERING ON TIME SERIES
SENSOR STREAMS
Spiros Papadimitriou
Rutgers University,
New Brunswick, NJ, USA
spapadim@business.rutgers.edu
Jimeng Sun
IBM Research,
Hawthorne, NY, USA
jimeng@us.ibm.com
Christos Faloutos
Carnegie Mellon University,
Pittsburgh, PA, USA
christos@cs.cmu.edu
Philip S. Yu
University of Illinois at Chicago,
Chicago, IL, USA
psyu@cs.uic.edu
Abstract
This chapter surveys fundamental tools for dimensionality reduction
and filtering of time series streams, illustrating what it takes to apply
them e
ciently and effectively to numerous problems. In particular, we
show how least-squares based techniques (auto-regression and principal
component analysis) can be successfully used to discover correlations
both across streams, as well as across time. We also broadly overview
work in the area of pattern discovery on time series streams, with ap-
plications in pattern discovery, dimensionality reduction, compression,
