Enhancing Changepoint Detection: Penalty Learning through Deep Learning Techniques

• URL: http://arxiv.org/abs/2408.00856v3
• Date: Wed, 18 Sep 2024 00:39:43 GMT
• Title: Enhancing Changepoint Detection: Penalty Learning through Deep Learning Techniques
• Authors: Tung L Nguyen, Toby Dylan Hocking,
• Abstract summary: This study introduces a novel deep learning method for predicting penalty parameters. It leads to demonstrably improved changepoint detection accuracy on large benchmark supervised labeled datasets.
• Score: 2.094821665776961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Changepoint detection, a technique for identifying significant shifts within data sequences, is crucial in various fields such as finance, genomics, medicine, etc. Dynamic programming changepoint detection algorithms are employed to identify the locations of changepoints within a sequence, which rely on a penalty parameter to regulate the number of changepoints. To estimate this penalty parameter, previous work uses simple models such as linear or tree-based models. This study introduces a novel deep learning method for predicting penalty parameters, leading to demonstrably improved changepoint detection accuracy on large benchmark supervised labeled datasets compared to previous methods.

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