Reconstruction of directed acyclic networks of dynamical systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations


Determining the relation structure of various interconnected entities from multiple time series data is of significant interest to many areas. Knowledge of such a structure can aid in identifying cause and effect relationships, clustering of similar entities, detecting representative elements for an aggregate and determining reduced order models. Current methods tend to treat observations in a static manner by modeling the measured time series as repeated realizations of as many random variables that are independent over time. This amounts to assume static relationships among the measurements, making these techniques ill-suited for detecting propagative and dynamic phenomena that can be fundamental for the understanding of the system. In this paper we extend techniques for the identification of networks of random variables connected through static relations to the case of random processes with dynamic relations. This is achieved by showing that the Wiener filter defines a relationship among jointly stationary stochastic processes that has the properties of a semi-graphoid.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
Number of pages6
StatePublished - Sep 11 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013


Other2013 1st American Control Conference, ACC 2013
Country/TerritoryUnited States
CityWashington, DC


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