An on-line algorithm for anomaly detection in trajectory data

2012 ◽  
Author(s):  
O. Rosen ◽  
A. Medvedev
Keyword(s):  
Anomaly Detection ◽  
Trajectory Data ◽  
On Line

Hybrid Group Anomaly Detection for Sequence Data: Application to Trajectory Data Analytics

2021 ◽  
pp. 1-12
Author(s):  
Asma Belhadi ◽  
Youcef Djenouri ◽  
Gautam Srivastava ◽  
Alberto Cano ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Anomaly Detection ◽  
Data Analytics ◽  
Sequence Data ◽  
Trajectory Data ◽  
Data Application ◽  
Hybrid Group

scholarly journals On-line anomaly detection and resilience in classifier ensembles

2013 ◽  
Vol 34 (15) ◽  
pp. 1916-1927 ◽  
Author(s):  
Hesam Sagha ◽  
Hamidreza Bayati ◽  
José del R. Millán ◽  
Ricardo Chavarriaga
Keyword(s):  
Anomaly Detection ◽  
Classifier Ensembles ◽  
On Line

scholarly journals A visual-numeric approach to clustering and anomaly detection for trajectory data

2015 ◽  
Vol 33 (3) ◽  
pp. 265-281 ◽  
Author(s):  
Dheeraj Kumar ◽  
James C. Bezdek ◽  
Sutharshan Rajasegarar ◽  
Christopher Leckie ◽  
Marimuthu Palaniswami
Keyword(s):  
Anomaly Detection ◽  
Trajectory Data

scholarly journals Adaptive Kernel Quantile Regression for Anomaly Detection

2009 ◽  
Vol 13 (3) ◽  
pp. 230-236
Author(s):  
Hiroyuki Moriguchi ◽  
◽  
Ichiro Takeuchi ◽  
Masayuki Karasuyama ◽  
Shin-ichi Horikawa ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Quantile Regression ◽  
Time Series Data ◽  
Data Distribution ◽  
Optimal Solution ◽  
Series Data ◽  
Future Time ◽  
Adaptive Kernel ◽  
On Line

In this paper, we study a problem of anomaly detection from time series-data. We use kernel quantile regression (KQR) to predict the extreme (such as 0.01 or 0.99) quantiles of the future time-series data distribution. It enables us to tell whether the probability of observing a certain time-series sequence is larger than, say, 1 percent or not. In this paper, we develop an efficient update algorithm of KQR in order to adapt the KQR in on-line manner. We propose a new algorithm that allows us to compute the optimal solution of the KQR when a new training pattern is inserted or deleted. We demonstrate the effectiveness of our methodology through numerical experiment using real-world time-series data.

Fast Anomaly Detection Algorithm for Hyperspectral Imagery Based on Line-by-line Processing

2017 ◽  
Vol 46 (4) ◽  
pp. 410003 ◽  
Author(s):  
付立婷 FU Li-ting ◽  
邓河 DENG He ◽  
刘春红 LIU Chun-hong
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Imagery ◽  
Detection Algorithm ◽  
On Line
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