Temporal causal modeling with graphical granger methods
By Andrew O. Arnold, Yan Liu, Naoki Abe. Knowledge Discovery and Data Mining, 2007
336 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/45a47053b2ccdefd912d7d4299ccbd30a1cd1caa
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yu Zheng
- Vipin Kumar
- Anuj Karpatne
- Jiawei Han
- Gowtham Atluri
- Charu C. Aggarwal
- Chao Zhang
- Huichu Zhang
In Statistics and Probability
- Andrew W. Lo
- Jiawei Han
- Aurelie C. Lozano
- Chao Zhang
- John V. Guttag
- Gartheeban Ganeshapillai
- Vikas Sindhwani
- Ha Quang Minh
Citations in the corpus
- Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality (2012, background): “We believe that this nonlinear generalization of graphical Granger Causality techniques (see [3, 22, 33] and references therein) may be of independent interest.”
- Spatio-Temporal Data Mining (2017, background): “These are further extended to capture sparsity [Arnold et al. 2007] and group structure [Lozano et al. 2009a] in the underlying model.”
- Learning Connections in Financial Time Series (2013, background): “Arnold et al. (2007) examines a host of algorithms that, loosely speaking, fall under the category of graphical Granger methods to quantify the connectedness in time series.”
- Memory Matters: A Case for Granger Causality in Climate Variability Studies (2018, methodology): “Granger causality has, in fact, been incorporated into these graphical models, creating an approach known as graphical Granger models (e.g., Arnold et al. 2007).”
- p-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data (2016, methodology): “[1] combine graphical techniques with the classic Granger causality, and propose a model to infer causality strengths for a large number of time series variables.”
- Sparse Vector Autoregressive Modeling (2012, methodology): “(2008); Arnold et al. (2008); Lozano et al. (2009); Haufe et al. (2010); Shojaie and Michailidis (2010); Song and Bickel (2011). The Lasso-VAR modeling approach has the advantage of performing model selection and parameter estimation simultaneously.”