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Found 3 papers, 1 papers with code
Causal Structure Recovery of Linear Dynamical Systems: An FFT based Approach
We demonstrate that the computational complexity of recovering the causation structure for the vector auto-regressive (VAR) model is $O(Tn^3N^2)$, where $n$ is the number of nodes, $T$ is the number of samples, and $N$ is the largest time-lag in the dependency between entities.
Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs
In this article, the optimal sample complexity of learning the underlying interactions or dependencies of a Linear Dynamical System (LDS) over a Directed Acyclic Graph (DAG) is studied.
Topology Identification under Spatially Correlated Noise
It is shown, under the assumption that the correlations are affine in nature, such network of nodal interactions is equivalent to a network with added agents, represented by nodes that are latent, where no corresponding time-series measurements are available; however, here all exogenous excitements are spatially (that is, across agents) uncorrelated.
