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» Visually mining and monitoring massive time series
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ICDM
2005
IEEE
271views Data Mining» more  ICDM 2005»
14 years 1 months ago
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of a longer time series that are maximally different to all the r...
Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu
VISSYM
2007
13 years 10 months ago
Multi-Resolution Techniques for Visual Exploration of Large Time-Series Data
Time series are a data type of utmost importance in many domains such as business management and service monitoring. We address the problem of visualizing large time-related data ...
Ming C. Hao, Umeshwar Dayal, Daniel A. Keim, Tobia...
ICANNGA
2009
Springer
133views Algorithms» more  ICANNGA 2009»
14 years 2 months ago
Visualizing Time Series State Changes with Prototype Based Clustering
Modern process and condition monitoring systems produce a huge amount of data which is hard to analyze manually. Previous analyzing techniques disregard time information and concen...
Markus Pylvänen, Sami Äyrämö, ...
INFOVIS
2005
IEEE
14 years 1 months ago
Importance-Driven Visualization Layouts for Large Time Series Data
Time series are an important type of data with applications in virtually every aspect of the real world. Often a large number of time series have to be monitored and analyzed in p...
Ming C. Hao, Umeshwar Dayal, Daniel A. Keim, Tobia...
SDM
2009
SIAM
291views Data Mining» more  SDM 2009»
14 years 4 months ago
Detection and Characterization of Anomalies in Multivariate Time Series.
Anomaly detection in multivariate time series is an important data mining task with applications to ecosystem modeling, network traffic monitoring, medical diagnosis, and other d...
Christopher Potter, Haibin Cheng, Pang-Ning Tan, S...