mlsanityData to 7 Oct 2026

Selective Inference for Changepoint detection by Recurrent Neural Network

arXiv 2311.14964 ↗First posted 25 Nov 2023stat.ML cs.LGcited by 4

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Abstract (arXiv)

In this study, we investigate the quantification of the statistical reliability of detected change points (CPs) in time series using a Recurrent Neural Network (RNN). Thanks to its flexibility, RNN holds the potential to effectively identify CPs in time series characterized by complex dynamics. However, there is an increased risk of erroneously detecting random noise fluctuations as CPs. The primary goal of this study is to rigorously control the risk of false detections by providing theoretically valid p-values to the CPs detected by RNN. To achieve this, we introduce a novel method based on the framework of Selective Inference (SI). SI enables valid inferences by conditioning on the event of hypothesis selection, thus mitigating selection bias. In this study, we apply SI framework to RNN-based CP detection, where characterizing the complex process of RNN selecting CPs is our main technical challenge. We demonstrate the validity and effectiveness of the proposed method through artificial and real data experiments.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 28 of its 53 references that are in the map. First posted on arXiv 25 Nov 2023.

Builds on

28 of 53 references.

References · 28

  1. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 213
  2. 2015Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inferencecited by 159
  3. 2016Hierarchical Multiscale Recurrent Neural Networkscited by 102
  4. 2013Exact post-selection inference, with application to the lassocited by 90
  5. 2020Time Series Change Point Detection with Self-Supervised Contrastive Predictive Codingcited by 58
  6. 2014Exact Post-Selection Inference for Sequential Regression Procedurescited by 47
  7. 2019Kernel Change-point Detection with Auxiliary Deep Generative Modelscited by 32
  8. 2015Scan B-Statistic for Kernel Change-Point Detectioncited by 28
  9. 2019Testing for a Change in Mean After Changepoint Detectioncited by 25
  10. 2020Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programmingcited by 25
  11. 2019Computing Valid p-values for Image Segmentation by Selective Inferencecited by 24
  12. 2021More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programmingcited by 24
16 more
  1. 2018More powerful post-selection inference, with application to the Lassocited by 22
  2. 2020Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representationcited by 21
  3. 2020Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inferencecited by 21
  4. 2018Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Datacited by 19
  5. 2023Valid P-Value for Deep Learning-Driven Salient Regioncited by 17
  6. 2021Tree-Values: selective inference for regression treescited by 15
  7. 2013Multiscale Change-Point Inferencecited by 13
  8. 2016Post Selection Inference with Kernelscited by 13
  9. 2017Selective Inference for Change Point Detection in Multi-dimensional Sequencescited by 11
  10. 2021Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inferencecited by 10
  11. 2022Exact Selective Inference with Randomizationcited by 6
  12. 2015Real-time financial surveillance via quickest change-point detection methodscited by 5
  13. 2017Selective inference for the problem of regions via multiscale bootstrapcited by 5
  14. 2016Exact Post-Selection Inference for Changepoint Detection and Other Generalized Lasso Problemscited by 3
  15. 2020Semi-supervised sequence classification through change point detectioncited by 2
  16. 2023Bounded P-values in Parametric Programming-based Selective Inferencecited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inferencecited by 3
  2. 2025Change Point Detection in the Frequency Domain with Statistical Reliabilitycited by 2
  3. 2025Consistent Kernel Change-Point Detection under m-Dependence for Text Segmentationcited by 0
  4. 2026Unsupervised Text Segmentation via Kernel Change-Point Detection on Sentence Embeddingscited by 0