[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126827-en":3,"doc-seo-126827-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126827,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Automatic change-point detection in time series via deep learning","Change-point detection in time series is difficult due to the wide variety of possible change types and data behaviors when no change occurs. Efficient statistical detection depends on these features, making it hard to pick a method matching a specific application. This paper develops an approach that automatically generates offline detection methods by training a neural network, supported by theory quantifying error rates as a function of training data size. Experiments show competitive mean-change detection under independent Gaussian noise, and substantial gains under autocorrelated or heavy-tailed noise, with strong accelerometer activity change results.","Journal of the Royal Statistical Society Series B: Statistical Methodology, 2024, 86, 273–285 [https://doi.org/10.1093/jrsssb/qkae004](https://doi.org/10.1093/jrsssb/qkae004)[ ](https://doi.org/10.1093/jrsssb/qkae004)Advance access publication 11 January 2024 Discussion Paper  \nAutomatic change-point detection in time series via deep learning  \nJie Li 1 , Paul Fearnhead2 , Piotr Fryzlewicz 1 and Tengyao Wang 1  \n1 Department of Statistics, London School of Economics and Political Science, London, UK 2 Department of Mathematics and Statistics, Lancaster University, Lancaster, UK  \nAddress for correspondence: Jie Li, Department of Statistics, London School of Economics and Political Science,  \nColumbia House, Houghton Street, London WC2A 2AE, UK. Email: [j.li196@lse.ac.uk](j.li196@lse.ac.uk)  \nRead before The Royal Statistical Society at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning’ held at the Society’s 2023 annual conference in Harrogate on Wednesday, 6 September 2023, the President, Dr Andrew Garrett, in the Chair.  \nAbstract  \nDetecting change points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our approach is motivated by many existing tests for the presence of a change point being representable by a simple neural network, and thus a neural network trained with sufficient data should have performance at least as good as these methods. We present theory that quantifies the error rate for such an approach, and how it depends on the amount of training data. Empirical results show that, even with limited training data, its performance is competitive with the standard cumulative sum (CUSUM) based classifier for detecting a change in mean when the noise is independent and Gaussian, and can substantially outperform it in the presence of auto-correlated or heavy-tailed noise. Our method also shows strong results in detecting and localizing changes in activity based on accelerometer data.  \nKeywords: automatic statistician, classification, likelihood-free inference, neural networks, structural breaks, supervised learning  \n1 Introduction  \nDetecting change points in data sequences is of interest in many application areas such as bioinformatics (Picard et al., 2005), climatology (Reeves et al., 2007), signal processing (Haynes et al., 2017), and neuroscience (Oh et al., 2005). In this work, we are primarily concerned with the problem of offline change-point detection, where the entire data is available to the analyst beforehand. Over the past few decades, various methodologies have been extensively studied in this area, see Killicket al. (2012), Jandhyala et al. (2013), Fryzlewicz (2014, 2023), Wang and Samworth (2018), Truong et al. (2020) and references therein. Most research on change-point detection has concentrated on detecting and localizing different types of change, e.g. change in mean (Fryzlewicz, 2014; Killick et al., 2012), variance (Gao et al., 2019; Li et al., 2015), median (Fryzlewicz, 2021), or slope (Baranowski et al., 2019; Fearnhead et al., 2019), amongst many others.  \nMany change-point detection methods are based upon modelling data when there is no change and when there is a single change, and then constructing an appropriate test statistic to detect the presence of a change (e.g. Fearnhead & Rigaill, 2020; James et al., 1987) . The form of a good test statistic will vary with our modelling assumptions and the type of change we wish to detect. This can lead to difficulties in practice. As we use new models, it is unlikely  \nReceived: November 7, 2022. Revised: June 9, 2023. Accep","cbCaihkt2XPDRC8o","https://ap.wps.com/l/cbCaihkt2XPDRC8o","pdf",3266986,1,67,"English","en",105,"# Abstract\n## Introduction\n## Problem motivation and offline detection setting\n## Supervised learning formulation via neural network\n## Test-statistic generation and theoretical error control\n## Empirical evaluation and applications","[{\"question\":\"What problem does the paper address in time series analysis?\",\"answer\":\"The paper addresses automatic offline change-point detection in time series, where the analyst has access to the full dataset and needs to identify moments when the data behavior changes.\"},{\"question\":\"How does the proposed method generate change-point detection rules?\",\"answer\":\"It trains a neural network on labeled examples of change points to classify whether a dataset contains the change of interest, thereby turning change-point detection into a supervised learning problem.\"},{\"question\":\"How does the method perform under different noise conditions?\",\"answer\":\"It is competitive with a standard CUSUM-based classifier for detecting mean changes when noise is independent and Gaussian, but it can substantially outperform CUSUM when noise is autocorrelated or heavy-tailed.\"}]","Automatic change-point detection in time series via deep learning | 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problem does the paper address in time series analysis?","Question",{"text":75,"@type":76},"The paper addresses automatic offline change-point detection in time series, where the analyst has access to the full dataset and needs to identify moments when the data behavior changes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method generate change-point detection rules?",{"text":80,"@type":76},"It trains a neural network on labeled examples of change points to classify whether a dataset contains the change of interest, thereby turning change-point detection into a supervised learning problem.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the method perform under different noise conditions?",{"text":84,"@type":76},"It is competitive with a standard CUSUM-based classifier for detecting mean changes when noise is independent and Gaussian, but it can substantially outperform CUSUM when noise is autocorrelated or 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