The objective of transfer learning is to enhance estimation and inference in
a target data by leveraging knowledge gained from additional sources. Recent
studies have explored transfer learning for independent observations in
complesso, high-dimensional models assuming sparsity, yet research on time series
models remains limited. Our focus is on transfer learning for sequences of
observations with temporal dependencies and a more intricate model parameter
struttura. Nello specifico, we investigate the vector autoregressive model (VAR),
a widely recognized model for time series data, where the transition matrix can
be deconstructed into a combination of a sparse matrix and a low-rank one. Noi
propose a new transfer learning algorithm tailored for estimating
high-dimensional VAR models characterized by low-rank and sparse structures.
Inoltre, we present a novel approach for selecting informative
observations from auxiliary datasets. Theoretical guarantees are established,
encompassing model parameter consistency, informative set selection, e il
asymptotic distribution of estimators under mild conditions. The latter
facilitates the construction of entry-wise confidence intervals for model
parametri. Finalmente, we demonstrate the empirical efficacy of our methodologies
through both simulated and real-world datasets.

Questo articolo esplora i giri e le loro implicazioni.

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