scholarly journals Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction

2014 ◽  
pp. 271-280 ◽  
Author(s):  
James Smith ◽  
Ilia Nouretdinov ◽  
Rachel Craddock ◽  
Charles Offer ◽  
Alexander Gammerman
Keyword(s):  
Anomaly Detection ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Conformal Prediction

scholarly journals Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression

2020 ◽  
Vol 32 (2) ◽  
pp. 218-233 ◽  
Author(s):  
Weiming Hu ◽  
Jun Gao ◽  
Bing Li ◽  
Ou Wu ◽  
Junping Du ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density

Layer-constrained variational autoencoding kernel density estimation model for anomaly detection

2020 ◽  
Vol 196 ◽  
pp. 105753 ◽  
Author(s):  
Peng Lv ◽  
Yanwei Yu ◽  
Yangyang Fan ◽  
Xianfeng Tang ◽  
Xiangrong Tong
Keyword(s):  
Anomaly Detection ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Estimation Model

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

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1363
Author(s):  
Michael R. Lindstrom ◽  
Hyuntae Jung ◽  
Denis Larocque
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Functional Data ◽  
Probability Distributions ◽  
Kernel Density ◽  
Principal Component ◽  
Probability Densities ◽  
Report Data

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 formally through treating each series as a point in a Hilbert space, placing a kernel at those points, and summing the kernels (a “point approach”), or through using Kernel Density Estimation to approximate the distributions of Fourier mode coefficients to infer a probability density (a “Fourier approach”). We refer to these approaches as Functional Kernel Density Estimation for Anomaly Detection as they both yield functionals that can score a time series for how anomalous it is. Both methods naturally handle missing data and apply to a variety of settings, performing well when compared with an outlyingness score derived from a boxplot method for functional data, with a Principal Component Analysis approach for functional data, and with the Functional Isolation Forest method. We illustrate the use of the proposed methods with aviation safety report data from the International Air Transport Association (IATA).

scholarly journals Author Correction: An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenzhong Shi ◽  
Chengzhuo Tong ◽  
Anshu Zhang ◽  
Bin Wang ◽  
Zhicheng Shi ◽  
...  
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Spatiotemporal Variations ◽  
Estimation Model

A Correction to this paper has been published: https://doi.org/10.1038/s42003-021-01924-6

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