Automatic change-point detection in time series via deep learning

Jie Li, Paul Fearnhead, Piotr Fryzlewicz and Tengyao Wang

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Detecting 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.

Keywords: automatic statistician; classification; likelihood-free inference; neural networks; structural breaks; supervised learning; e High End Computing Cluster at Lancaster University; and EPSRC grants EP/V053590/1; EP/V053639/1 and EP/T02772X/1 (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2024-04-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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Published in Journal of the Royal Statistical Society. Series B: Statistical Methodology, 1, April, 2024, 86(2), pp. 273 - 285. ISSN: 1369-7412

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:120083

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