2012
DOI: 10.1145/2337542.2337561
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Learning Causal Relations in Multivariate Time Series Data

Abstract: Many applications naturally involve time series data and the vector autoregression (VAR), and the structural VAR (SVAR) are dominant tools to investigate relations between variables in time series. In the first part of this work, we show that the SVAR method is incapable of identifying contemporaneous causal relations for Gaussian process. In addition, least squares estimators become unreliable when the scales of the problems are large and observations are limited. In the remaining part, we propose an approach… Show more

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Cited by 6 publications
(2 citation statements)
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“…Burg suggests that recursive algorithm estimated by the AR(P) model is the most practical one [29], while Hannan proposes time series with multidimensional linear stationary RMA p; q ðÞ . The times series model mainly includes the autoregressive model and the moving average model [30][31][32], and generally the modeling steps are as follows.…”
Section: Time Series Model (Tsm)mentioning
confidence: 99%
“…Burg suggests that recursive algorithm estimated by the AR(P) model is the most practical one [29], while Hannan proposes time series with multidimensional linear stationary RMA p; q ðÞ . The times series model mainly includes the autoregressive model and the moving average model [30][31][32], and generally the modeling steps are as follows.…”
Section: Time Series Model (Tsm)mentioning
confidence: 99%
“…Such relationships between two data variables usually exhibit uncertainty due to the nature of data or imperfect observations, and thus require probabilistic modeling. Even more challenging, such causal relationship among large numbers of variables may not be known, and thus a challenge is to determine or to learn the knowledge discovery structure [6][19] [20].…”
Section: Introductionmentioning
confidence: 99%