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GFKL
2005
Springer
133views Data Mining» more  GFKL 2005»
13 years 11 months ago
Finding Persisting States for Knowledge Discovery in Time Series
Abstract. Knowledge Discovery in time series usually requires symbolic time series. Many discretization methods that convert numeric time series to symbolic time series ignore the ...
Fabian Mörchen, Alfred Ultsch
FSKD
2005
Springer
267views Fuzzy Logic» more  FSKD 2005»
13 years 11 months ago
Preventing Meaningless Stock Time Series Pattern Discovery by Changing Perceptually Important Point Detection
Discovery of interesting or frequently appearing time series patterns is one of the important tasks in various time series data mining applications. However, recent research critic...
Tak-Chung Fu, Fu-Lai Chung, Robert W. P. Luk, Chak...
JMLR
2011
93views more  JMLR 2011»
13 years 9 days ago
Time Series Analysis with the Causality Workbench
The Causality Workbench project is an environment to test causal discovery algorithms. Via a web portal (http://clopinet.com/causality), it provides a number of resources, includi...
Isabelle Guyon, Alexander Satnikov, Constantin F. ...
SDM
2010
SIAM
156views Data Mining» more  SDM 2010»
13 years 6 months ago
Unsupervised Discovery of Abnormal Activity Occurrences in Multi-dimensional Time Series, with Applications in Wearable Systems
We present a method for unsupervised discovery of abnormal occurrences of activities in multi-dimensional time series data. Unsupervised activity discovery approaches differ from ...
Alireza Vahdatpour, Majid Sarrafzadeh
KDD
2003
ACM
146views Data Mining» more  KDD 2003»
14 years 5 months ago
Probabilistic discovery of time series motifs
Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalize...
Bill Yuan-chi Chiu, Eamonn J. Keogh, Stefano Lonar...