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Kernel analog forecasting (KAF) is a powerful methodology for data-driven, non-parametric forecasting of dynamically generated time series data. This approach has a rigorous foundation in Koopman operator theory and it produces good forecasts in prac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c282421dca3d93dfd89c732de13f8a3e
http://arxiv.org/abs/2109.09703
http://arxiv.org/abs/2109.09703
Autor:
Rachel Ward, Amelia Henriksen
Suppose $\{ X_k \}_{k \in \mathbb{Z}}$ is a sequence of bounded independent random matrices with common dimension $d\times d$ and common expectation $\mathbb{E}[ X_k ]= X$. Under these general assumptions, the normalized random matrix product $$Z_n =
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b72d1c38d274ea07cc8be49db4ad2f68
Publikováno v:
SIAM Journal on Applied Dynamical Systems; 2023, Vol. 22 Issue 2, p527-558, 32p