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...

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Detalles Bibliográficos
Autores principales: Lindstrom, Michael R., Jung, Hyuntae, Larocque, Denis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759980/
https://www.ncbi.nlm.nih.gov/pubmed/33266340
http://dx.doi.org/10.3390/e22121363

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