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IDA
2003
Springer
14 years 23 days ago
Learning Dynamic Bayesian Networks from Multivariate Time Series with Changing Dependencies
Abstract. Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any mode...
Allan Tucker, Xiaohui Liu
FQAS
2004
Springer
146views Database» more  FQAS 2004»
13 years 11 months ago
Discovering Representative Models in Large Time Series Databases
The discovery of frequently occurring patterns in a time series could be important in several application contexts. As an example, the analysis of frequent patterns in biomedical ...
Simona E. Rombo, Giorgio Terracina
TMI
2010
175views more  TMI 2010»
13 years 2 months ago
Spatially Adaptive Mixture Modeling for Analysis of fMRI Time Series
Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In [1, 2...
Thomas Vincent, Laurent Risser, Philippe Ciuciu
JASIS
2010
121views more  JASIS 2010»
13 years 6 months ago
Linear time series models for term weighting in information retrieval
Common measures of term importance in information retrieval (IR) rely on counts of term frequency; rare terms receive higher weight in document ranking than common terms receive. ...
Miles Efron
SDM
2012
SIAM
355views Data Mining» more  SDM 2012»
11 years 10 months ago
Granger Causality Analysis in Irregular Time Series
Learning temporal causal structures between time series is one of the key tools for analyzing time series data. In many real-world applications, we are confronted with Irregular T...
Mohammad Taha Bahadori, Yan Liu