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KDD
2009
ACM
364views Data Mining» more  KDD 2009»
14 years 5 months ago
Causality quantification and its applications: structuring and modeling of multivariate time series
Time series prediction is an important issue in a wide range of areas. There are various real world processes whose states vary continuously, and those processes may have influenc...
Takashi Shibuya, Tatsuya Harada, Yasuo Kuniyoshi
JMLR
2011
187views more  JMLR 2011»
12 years 11 months ago
Robust Statistics for Describing Causality in Multivariate Time Series
A widely agreed upon definition of time series causality inference, established in the seminal 1969 article of Clive Granger (1969), is based on the relative ability of the histor...
Florin Popescu
JMLR
2010
194views more  JMLR 2010»
12 years 11 months ago
Graphical Gaussian modelling of multivariate time series with latent variables
In time series analysis, inference about causeeffect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal struc...
Michael Eichler
ICML
2010
IEEE
13 years 5 months ago
Learning Temporal Causal Graphs for Relational Time-Series Analysis
Learning temporal causal graph structures from multivariate time-series data reveals important dependency relationships between current observations and histories, and provides a ...
Yan Liu 0002, Alexandru Niculescu-Mizil, Aurelie C...
BC
2007
113views more  BC 2007»
13 years 4 months ago
Akaike causality in state space
We present a new approach of explaining partial causality in multivariate fMRI time series by a state space model. A given single time series can be divided into two noise-driven ...
Kin Foon Kevin Wong, Tohru Ozaki