This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2009 WRI World Congress on Computer Science and Information Engineering
A Novel Algorithm for Identifying Patterns from Multisensor Time Series
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
Pattern identification from multisensor time series is an important problem in many measurement, detection, and monitoring related applications. This paper introduces a generic approach to detect varying-length patterns and identify their class using predefined templates. In reality, the measured phenomena representing the same class can occur in slightly different ways which makes the resulting patterns vary in "shape" and time. A template-based approach calls for a sound processing of the manifold sensor signals in order to perform the needed comparisons reasonably. We use a template-specific quantization to normalize the signals and to manage their fluctuations. Dynamic time warping (DTW) handles their temporal variations. Our algorithm detects multiple patterns from a single DTW matrix in a computationally efficient way, without windowing. The background of this paper is in aeronautical fatigue research, and we evaluate the proposed algorithm with flight maneuver identification from flight monitoring data.
Index Terms:
Pattern detection, pattern identification, multisensor data, time series, DTW, dynamic time warping
Citation:
Marja Ruotsalainen, Juha Jylhä, Juho Vihonen, Ari Visa, "A Novel Algorithm for Identifying Patterns from Multisensor Time Series," csie, vol. 6, pp.100-105, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
Usage of this product signifies your acceptance of the Terms of Use.