Finding the most unusual time series subsequence: Algorithms and applications

Knowledge and Information Systems

Volumn 11, Issue 1, 2007, Pages 1-27

  Keogh, Eamonn ^a   Lin, Jessica ^b   Lee, Sang Hee ^c   Van Herle, Helga ^d  

^a University of California Riverside   (United States)

^b George Mason University   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Anomaly detection; Clustering; Time series data mining

Indexed keywords

ANOMALY DETECTION; TIME SERIES; TIME SERIES ANALYSIS;

ANOMALY DETECTOR; ANOMALY-DETECTION ALGORITHMS; BRUTE FORCE ALGORITHMS; CLUSTERING; ORDERS OF MAGNITUDE; SIMPLE ALGORITHM; TIME SERIES DATA MINING; TIME SERIES SUBSEQUENCES;

DATA MINING;

EID: 33845271242     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-006-0034-6     Document Type: Article

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