Exact variable-length anomaly detection algorithm for univariate and multivariate time series
- 31 July 2018
- journal article
- research article
- Published by Springer Science and Business Media LLC in Data Mining and Knowledge Discovery
- Vol. 32 (6), 1806-1844
- https://doi.org/10.1007/s10618-018-0569-7
Abstract
No abstract availableKeywords
Funding Information
- Semiconductor Research Corporation (2015-IN-2611)
This publication has 33 references indexed in Scilit:
- Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning StrategyPublished by Association for Computing Machinery (ACM) ,2015
- Outlier Detection for Temporal DataSynthesis Lectures on Data Mining and Knowledge Discovery, 2014
- Fault detection and diagnosis using Principal Component Analysis of vibration data from a reciprocating compressorPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Unsupervised feature selection for multi-cluster dataPublished by Association for Computing Machinery (ACM) ,2010
- On clustering massive text and categorical data streamsKnowledge and Information Systems, 2009
- Detection and Characterization of Anomalies in Multivariate Time SeriesPublished by Society for Industrial & Applied Mathematics (SIAM) ,2009
- Outliers Detection in Multivariate Time Series by Independent Component AnalysisNeural Computation, 2007
- Outlier Detection in Multivariate Time Series by Projection PursuitJournal of the American Statistical Association, 2006
- Discovering cluster-based local outliersPattern Recognition Letters, 2003
- Identification of OutliersPublished by Springer Science and Business Media LLC ,1980