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» Unsupervised Outlier Detection in Time Series Data
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VLDB
2007
ACM
179views Database» more  VLDB 2007»
14 years 7 months ago
Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data
Market analysis is a representative data analysis process with many applications. In such an analysis, critical numerical measures, such as profit and sales, fluctuate over time a...
Xiaolei Li, Jiawei Han
FSKD
2005
Springer
267views Fuzzy Logic» more  FSKD 2005»
14 years 27 days ago
Preventing Meaningless Stock Time Series Pattern Discovery by Changing Perceptually Important Point Detection
Discovery of interesting or frequently appearing time series patterns is one of the important tasks in various time series data mining applications. However, recent research critic...
Tak-Chung Fu, Fu-Lai Chung, Robert W. P. Luk, Chak...
ICML
2009
IEEE
14 years 8 months ago
Detecting the direction of causal time series
We propose a method that detects the true direction of time series, by fitting an autoregressive moving average model to the data. Whenever the noise is independent of the previou...
Arthur Gretton, Bernhard Schölkopf, Dominik J...
ISPA
2007
Springer
14 years 1 months ago
ECG Anomaly Detection via Time Series Analysis
—Recently, wireless sensor networks have been proposed for assisted living and residential monitoring. In such networks, physiological sensors are used to monitor vital signs e.g...
Mooi Choo Chuah, Fen Fu
CSDA
2008
158views more  CSDA 2008»
13 years 7 months ago
Outlier identification in high dimensions
A computationally fast procedure for identifying outliers is presented, that is particularly effective in high dimensions. This algorithm utilizes simple properties of principal c...
Peter Filzmoser, Ricardo A. Maronna, Mark Werner