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» Mining Interval Time Series
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ICDM
2006
IEEE
193views Data Mining» more  ICDM 2006»
14 years 3 months ago
Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features
Several spatio-temporal data collected in many applications, such as fMRI data in medical applications, can be represented as a Multivariate Time Series (MTS) matrix with m rows (...
Hyunjin Yoon, Cyrus Shahabi
ICDM
2002
IEEE
130views Data Mining» more  ICDM 2002»
14 years 2 months ago
Unsupervised Segmentation of Categorical Time Series into Episodes
This paper describes an unsupervised algorithm for segmenting categorical time series into episodes. The VOTING-EXPERTS algorithm first collects statistics about the frequency an...
Paul R. Cohen, Brent Heeringa, Niall M. Adams
ICDM
2006
IEEE
193views Data Mining» more  ICDM 2006»
14 years 3 months ago
Local Correlation Tracking in Time Series
We address the problem of capturing and tracking local correlations among time evolving time series. Our approach is based on comparing the local auto-covariance matrices (via the...
Spiros Papadimitriou, Jimeng Sun, Philip S. Yu
SDM
2009
SIAM
343views Data Mining» more  SDM 2009»
14 years 6 months ago
Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation.
Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been acti...
Masashi Sugiyama, Yoshinobu Kawahara
ADMA
2006
Springer
112views Data Mining» more  ADMA 2006»
14 years 3 months ago
Finding Time Series Discords Based on Haar Transform
The problem of finding anomaly has received much attention recently. However, most of the anomaly detection algorithms depend on an explicit definition of anomaly, which may be i...
Ada Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. K...