Autoregressive Identification of Kronecker Graphical Models
This work addresses a specific modeling problem in time series analysis, likely incremental in nature.
The authors tackled the problem of estimating a Kronecker graphical model for an autoregressive Gaussian process, proposing a Bayesian approach and testing it with numerical experiments and urban pollution data.
We address the problem to estimate a Kronecker graphical model corresponding to an autoregressive Gaussian stochastic process. The latter is completely described by the power spectral density function whose inverse has support which admits a Kronecker product decomposition. We propose a Bayesian approach to estimate such a model. We test the effectiveness of the proposed method by some numerical experiments. We also apply the procedure to urban pollution monitoring data.