Feb. 28, 2024, 5:44 a.m. | Nicolai Palm, Thomas Nagler

cs.LG updates on arXiv.org arxiv.org

arXiv:2310.19683v2 Announce Type: replace-cross
Abstract: Resampling methods such as the bootstrap have proven invaluable in the field of machine learning. However, the applicability of traditional bootstrap methods is limited when dealing with large streams of dependent data, such as time series or spatially correlated observations. In this paper, we propose a novel bootstrap method that is designed to account for data dependencies and can be executed online, making it particularly suitable for real-time applications. This method is based on an …

abstract arxiv bootstrap cs.lg data machine machine learning novel paper resampling series stat.co stat.me stat.ml time series type

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