Score-Based Quickest Change Detection and Fault Identification for Multi-Stream Signals
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911251074908160 |
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| author | Chen, Wuxia Moushegian, Sean Tarokh, Vahid Banerjee, Taposh |
| author_facet | Chen, Wuxia Moushegian, Sean Tarokh, Vahid Banerjee, Taposh |
| contents | This paper introduces an approach to multi-stream quickest change detection and fault isolation for unnormalized and score-based statistical models. Traditional optimal algorithms in the quickest change detection literature require explicit pre-change and post-change distributions to calculate the likelihood ratio of the observations, which can be computationally expensive for higher-dimensional data and sometimes even infeasible for complex machine learning models. To address these challenges, we propose the min-SCUSUM method, a Hyvarinen score-based algorithm that computes the difference of score functions in place of log-likelihood ratios. We provide a delay and false alarm analysis of the proposed algorithm, showing that its asymptotic performance depends on the Fisher divergence between the pre- and post-change distributions. Furthermore, we establish an upper bound on the probability of fault misidentification in distinguishing the affected stream from the unaffected ones. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03967 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Score-Based Quickest Change Detection and Fault Identification for Multi-Stream Signals Chen, Wuxia Moushegian, Sean Tarokh, Vahid Banerjee, Taposh Signal Processing Statistics Theory Methodology This paper introduces an approach to multi-stream quickest change detection and fault isolation for unnormalized and score-based statistical models. Traditional optimal algorithms in the quickest change detection literature require explicit pre-change and post-change distributions to calculate the likelihood ratio of the observations, which can be computationally expensive for higher-dimensional data and sometimes even infeasible for complex machine learning models. To address these challenges, we propose the min-SCUSUM method, a Hyvarinen score-based algorithm that computes the difference of score functions in place of log-likelihood ratios. We provide a delay and false alarm analysis of the proposed algorithm, showing that its asymptotic performance depends on the Fisher divergence between the pre- and post-change distributions. Furthermore, we establish an upper bound on the probability of fault misidentification in distinguishing the affected stream from the unaffected ones. |
| title | Score-Based Quickest Change Detection and Fault Identification for Multi-Stream Signals |
| topic | Signal Processing Statistics Theory Methodology |
| url | https://arxiv.org/abs/2511.03967 |