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» Assumption-Free Anomaly Detection in Time Series
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SSDBM
2005
IEEE
175views Database» more  SSDBM 2005»
13 years 10 months ago
Assumption-Free Anomaly Detection in Time Series
Recent advancements in sensor technology have made it possible to collect enormous amounts of data in real time. However, because of the sheer volume of data most of it will never...
Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamon...
SDM
2009
SIAM
291views Data Mining» more  SDM 2009»
14 years 1 months ago
Detection and Characterization of Anomalies in Multivariate Time Series.
Anomaly detection in multivariate time series is an important data mining task with applications to ecosystem modeling, network traffic monitoring, medical diagnosis, and other d...
Christopher Potter, Haibin Cheng, Pang-Ning Tan, S...
ADBIS
2006
Springer
200views Database» more  ADBIS 2006»
13 years 10 months ago
Anomaly Detection Using Unsupervised Profiling Method in Time Series Data
The anomaly detection problem has important applications in the field of fraud detection, network robustness analysis and intrusion detection. This paper is concerned with the prob...
Zakia Ferdousi, Akira Maeda
ADMA
2006
Springer
112views Data Mining» more  ADMA 2006»
13 years 10 months ago
Finding Time Series Discords Based on Haar Transform
The problem of finding anomaly has received much attention recently. However, most of the anomaly detection algorithms depend on an explicit definition of anomaly, which may be i...
Ada Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. K...
SDM
2008
SIAM
206views Data Mining» more  SDM 2008»
13 years 6 months ago
Latent Variable Mining with Its Applications to Anomalous Behavior Detection
In this paper, we propose a new approach to anomaly detection by looking at the latent variable space to make the first step toward latent anomaly detection. Most conventional app...
Shunsuke Hirose, Kenji Yamanishi