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» Detecting the direction of causal time series
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ICML
2009
IEEE
14 years 5 months ago
Detecting the direction of causal time series
We propose a method that detects the true direction of time series, by fitting an autoregressive moving average model to the data. Whenever the noise is independent of the previou...
Arthur Gretton, Bernhard Schölkopf, Dominik J...
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
ICANN
2007
Springer
13 years 11 months ago
Information Theoretic Derivations for Causality Detection: Application to Human Gait
As a causality criterion we propose the conditional relative entropy. The relationship with information theoretic functionals mutual information and entropy is established. The con...
Gert Van Dijck, Jo Van Vaerenbergh, Marc M. Van Hu...
SDM
2009
SIAM
343views Data Mining» more  SDM 2009»
14 years 2 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
RECOMB
2009
Springer
14 years 5 months ago
A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series
Abstract. Understanding the regulatory mechanisms that are responsible for an organism's response to environmental changes is an important question in molecular biology. A fir...
Oliver Stegle, Katherine J. Denby, David L. Wild, ...