
Learning Network of Multivariate Hawkes Processes: A Time Series Approach
Learning the influence structure of multiple time series data is of grea...
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Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages
Identifying risk spillovers in financial markets is of great importance ...
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From dependency to causality: a machine learning approach
The relationship between statistical dependency and causality lies at th...
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Inductive Granger Causal Modeling for Multivariate Time Series
Granger causal modeling is an emerging topic that can uncover Granger ca...
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Variablelag Granger Causality for Time Series Analysis
Granger causality is a fundamental technique for causal inference in tim...
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Causal Modelling of HeavyTailed Variables and Confounders with Application to River Flow
Confounding variables are a recurrent challenge for causal discovery and...
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NonAsymptotic Guarantees for Robust Identification of Granger Causality via the LASSO
Granger causality is among the widely used datadriven approaches for ca...
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Normalized multivariate time series causality analysis and causal graph reconstruction
Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as real physical notion so as to formulate it from first principles, however, seems to go unnoticed. This study introduces to the community this line of work, with a longdue generalization of the information flowbased bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized, and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of selfloops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noisetosignal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a huge challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.
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