Deep Autoencoding Kernel Density Estimation Model for Anomaly Detection

2021 ◽  
Vol 11 (03) ◽  
pp. 682-689
Author(s):  
鹏 吕
Keyword(s):  
Anomaly Detection ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Estimation Model

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 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

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

scholarly journals Forecasting COVID-19 onset risk and evaluating spatiotemporal variations of the lockdown effect in China

2020 ◽  
Author(s):  
Wenzhong Shi ◽  
Chengzhuo Tong ◽  
Anshu Zhang ◽  
Bin Wang ◽  
Zhicheng Shi ◽  
...  
Keyword(s):  
High Resolution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Symptom Onset ◽  
Kernel Density ◽  
Spatiotemporal Variations ◽  
Estimation Model ◽  
Chinese Cities ◽  
Medium Risk ◽  
The City

Abstract It is important to forecast the risk of COVID-19 symptom onset and thereby evaluate how effectively the city lockdown measure could reduce this risk. This study is a first comprehensive, high-resolution investigation of spatiotemporal heterogeneities in the effect of the Wuhan lockdown on the risk of COVID-19 symptom onset in all 347 Chinese cities. An extended Weight Kernel Density Estimation model was developed to predict the COVID-19 onset risk under two scenarios (i.e., with and without Wuhan lockdown). The Wuhan lockdown, compared with the scenario without lockdown implementation, delayed the arrival of the COVID-19 onset risk peak for 1-2 days in general and lowered risk peak values among all cities. The decrease of the onset risk attributed to the lockdown was more than 8% in over 40% of Chinese cities, and up to 21.3% in some cities. Lockdown was the most effective in areas with medium risk before lockdown.

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).

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