Noise-contrastive Online Change Point Detection
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866918400551288832 |
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| author | Puchkin, Nikita Goldman, Artur Yakovlev, Konstantin Dzis, Valeriia Vinogradova, Uliana |
| author_facet | Puchkin, Nikita Goldman, Artur Yakovlev, Konstantin Dzis, Valeriia Vinogradova, Uliana |
| contents | We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to flexible algorithms suitable for both parametric and nonparametric scenarios. We prove non-asymptotic bounds on the average running length of the procedure and its expected detection delay. The efficiency of the algorithm is illustrated with numerical experiments on synthetic and real-world data sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_10143 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Noise-contrastive Online Change Point Detection Puchkin, Nikita Goldman, Artur Yakovlev, Konstantin Dzis, Valeriia Vinogradova, Uliana Machine Learning Statistics Theory Methodology We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to flexible algorithms suitable for both parametric and nonparametric scenarios. We prove non-asymptotic bounds on the average running length of the procedure and its expected detection delay. The efficiency of the algorithm is illustrated with numerical experiments on synthetic and real-world data sets. |
| title | Noise-contrastive Online Change Point Detection |
| topic | Machine Learning Statistics Theory Methodology |
| url | https://arxiv.org/abs/2206.10143 |