When lookout sees crackle: Anomaly detection via kernel density estimation
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866914416709074944 |
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| author | Hyndman, Rob J Kandanaarachchi, Sevvandi Turner, Katharine |
| author_facet | Hyndman, Rob J Kandanaarachchi, Sevvandi Turner, Katharine |
| contents | We present an updated version of lookout -- an algorithm for detecting anomalies using kernel density estimates with bandwidth based on Rips death diameters -- with theoretical guarantees. The kernel density estimator for updated lookout is shown to be consistent, and the proposed multivariate scaling is robust and efficient. We show our updated algorithm performs better than the previous version on diverse examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22636 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | When lookout sees crackle: Anomaly detection via kernel density estimation Hyndman, Rob J Kandanaarachchi, Sevvandi Turner, Katharine Methodology We present an updated version of lookout -- an algorithm for detecting anomalies using kernel density estimates with bandwidth based on Rips death diameters -- with theoretical guarantees. The kernel density estimator for updated lookout is shown to be consistent, and the proposed multivariate scaling is robust and efficient. We show our updated algorithm performs better than the previous version on diverse examples. |
| title | When lookout sees crackle: Anomaly detection via kernel density estimation |
| topic | Methodology |
| url | https://arxiv.org/abs/2603.22636 |