Authors' reply to the discussion of 'Automatic change-point detection in time series via deep learning' at the discussion meeting on 'Probabilistic and statistical aspects of machine learning'

Li, Jie, Fearnhead, Paul, Fryzlewicz, Piotr ORCID: 0000-0002-9676-902X and Wang, Tengyao ORCID: 0000-0003-2072-6645 (2024) Authors' reply to the discussion of 'Automatic change-point detection in time series via deep learning' at the discussion meeting on 'Probabilistic and statistical aspects of machine learning'. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 86 (2). 332 - 334. ISSN 1369-7412

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Abstract

We would like to thank the proposer, seconder, and all discussants for their time in reading our article and their thought-provoking comments. We are glad to find a broad consensus that neural-network-based approach offers a flexible framework for automatic change-point analysis. There are a number of common themes to the comments, and we have therefore structured our response around the topics of the theory, training, the importance of standardization and possible extensions, before addressing some of the remaining individual comments.

Item Type:	Article
Official URL:	https://academic.oup.com/jrsssb
Additional Information:	© 2024 The Authors
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	25 Apr 2024 13:54
Last Modified:	01 May 2024 03:04
URI:	http://eprints.lse.ac.uk/id/eprint/122793

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