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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'

Author

Listed:
  • Li, Jie
  • Fearnhead, Paul
  • Fryzlewicz, Piotr
  • Wang, Tengyao

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.

Suggested Citation

  • Li, Jie & Fearnhead, Paul & Fryzlewicz, Piotr & Wang, Tengyao, 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'," LSE Research Online Documents on Economics 122793, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:122793
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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