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Center for Nonlinear Studies |
Determining interrelatedness structure of various 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, identification of representative elements and model reduction. In this talk, a methodology for identifying the interrelatedness structure of dynamically related time series data based on passive observations will be presented. The framework will allow for the presence of loops in the connectivity structure of the network. The quality of the reconstruction will be quantified. Results on the how the sparsity of multivariate Wiener filter, the Granger filter and the causal Wiener filter depend on the network structure will be presented. In a subsequent problem, assuming the interconnectedness structure is known, methods will be presented for identifying linear time-invariant dynamics between a specific pair of entities in the presence co agents whose activity is not monitored. Links to robustness of identification will be exemplified. Here, onnections to graphical models with notions of independence posed by d-separation will be highlighted.
Host: Deep Deka