Feb. 13, 2024, 5:45 a.m. | Augusto Santos Diogo Rente Rui Seabra Jos\'e M. F. Moura

cs.LG updates on arXiv.org arxiv.org

This paper considers learning the hidden causal network of a linear networked dynamical system (NDS) from the time series data at some of its nodes -- partial observability. The dynamics of the NDS are driven by colored noise that generates spurious associations across pairs of nodes, rendering the problem much harder. To address the challenge of noise correlation and partial observability, we assign to each pair of nodes a feature vector computed from the time series data of observed nodes. …

cs.lg data dynamics hidden linear network nodes noise observability paper series stat.me systems time series

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