2009 GroupedGraphicalGrangerModeling

• (Lozano et al., 2009) ⇒ Aurelie C. Lozano, Naoki Abe, Yan Liu, and Saharon Rosset. (2009). “Grouped Graphical Granger Modeling Methods for Temporal Causal Modeling.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557085

Subject Headings: Timeseries Analysis, Graphical Granger Modeling.

Cited By

• http://scholar.google.com/scholar?q=%22Grouped+graphical+Granger+modeling+methods+for+temporal+causal+modeling%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557085&preflayout=flat#citedby

Quotes

Abstract

We develop and evaluate an approach to causal modeling based on time series data, collectively referred to as “grouped graphical Granger modeling methods.” Graphical Granger modeling uses graphical modeling techniques on time series data and invokes the notion of “Granger causality” to make assertions on causality among a potentially large number of time series variables through inference on time-lagged effects. The present paper proposes a novel enhancement to the graphical Granger methodology by developing and applying families of regression methods that are sensitive to group information among variables, to leverage the group structure present in the lagged temporal variables according to the time series they belong to. Additionally, we propose a new family of algorithms we call group boosting, as an improved component of grouped graphical Granger modeling over the existing regression methods with grouped variable selection in the literature (e.g group Lasso). The introduction of group boosting methods is primarily motivated by the need to deal with non-linearity in the data. We perform empirical evaluation to confirm the advantage of the grouped graphical Granger methods over the standard (non-grouped) methods, as well as that specific to the methods based on group boosting. This advantage is also demonstrated for the real world application of gene regulatory network discovery from time-course microarray data.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 GroupedGraphicalGrangerModeling	Saharon Rosset Naoki Abe Yan Liu Aurelie C. Lozano			Grouped Graphical Granger Modeling Methods for Temporal Causal Modeling		KDD-2009 Proceedings		10.1145/1557019.1557085		2009