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Functional Kernel Density Estimation: Point and Fourier Approaches to Time Series Anomaly Detection
We present an unsupervised method to detect anomalous time series among a collection of time series. To do so, we extend traditional Kernel Density Estimation for estimating probability distributions in Euclidean space to Hilbert spaces. The estimated probability densities we derive can be obtained...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759980/ https://www.ncbi.nlm.nih.gov/pubmed/33266340 http://dx.doi.org/10.3390/e22121363 |