DocumentCode :
3106917
Title :
Fast Relevance Discovery in Time Series
Author :
Perng, Chang-Shing ; Wang, Haixun ; Ma, Sheng
Author_Institution :
IBM Res., Hawthorne, NY
fYear :
2006
fDate :
18-22 Dec. 2006
Firstpage :
1016
Lastpage :
1020
Abstract :
In this paper, we propose to model time series from a new angle: state transition points. When fluctuation of values in a time series crosses a certain point, it may trigger state transition in the system, which may lead to abrupt changes in many other time series. The concept of state transition points is essential in understanding the behavior of the time series and the behavior of the system. The new measure is robust and is capable of discovering correlations that Pearson´s coefficient cannot reveal. We propose efficient algorithms to identify state transition points and to compute correlation between two time series. We also introduce some triangular inequalities to efficiently find highly correlated time series among many time series.
Keywords :
binary sequences; data analysis; time series; Pearson coefficient; fast relevance discovery; state transition points; time series; triangular inequalities; Application software; Bifurcation; Binary sequences; Condition monitoring; Fluctuations; Mutual information; Robustness; Scattering; Time measurement; Time series analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining, 2006. ICDM '06. Sixth International Conference on
Conference_Location :
Hong Kong
ISSN :
1550-4786
Print_ISBN :
0-7695-2701-7
Type :
conf
DOI :
10.1109/ICDM.2006.71
Filename :
4053145
Link To Document :
بازگشت