Functional Kernel Density Estimation: Point and Fourier Approaches to Time Series Anomaly Detection

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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Bibliographic Details
Main Authors: Michael R. Lindstrom, Hyuntae Jung, Denis Larocque
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Entropy
Subjects:
time series
anomaly detection
unsupervised learning
kernel density estimation
missing data
Online Access:https://www.mdpi.com/1099-4300/22/12/1363

Internet

https://www.mdpi.com/1099-4300/22/12/1363

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