%0 Conference Paper %1 1565666 %A Chan, P. K. %A Mahoney, M. V. %B Fifth IEEE International Conference on Data Mining (ICDM'05) %D 2005 %K anomaly-detection time-series %P 8 pp.- %R 10.1109/ICDM.2005.101 %T Modeling multiple time series for anomaly detection %U https://ieeexplore.ieee.org/document/1565666/ %X Our goal is to generate comprehensible and accurate models from multiple time series for anomaly detection. The models need to produce anomaly scores in an online manner for real-life monitoring tasks. We introduce three algorithms that work in a constructed feature space and evaluate them with a real data set from the NASA shuttle program. Our offline and online evaluations indicate that our algorithms can be more accurate than two existing algorithms.

@inproceedings{1565666, abstract = {Our goal is to generate comprehensible and accurate models from multiple time series for anomaly detection. The models need to produce anomaly scores in an online manner for real-life monitoring tasks. We introduce three algorithms that work in a constructed feature space and evaluate them with a real data set from the NASA shuttle program. Our offline and online evaluations indicate that our algorithms can be more accurate than two existing algorithms.}, added-at = {2019-06-10T18:07:16.000+0200}, author = {{Chan}, P. K. and {Mahoney}, M. V.}, biburl = {https://www.bibsonomy.org/bibtex/20ef3d65c9be2ca636891c0eb4282c1db/nonancourt}, booktitle = {Fifth IEEE International Conference on Data Mining (ICDM'05)}, doi = {10.1109/ICDM.2005.101}, interhash = {2a294b9aea36c265736c8827606acfa6}, intrahash = {0ef3d65c9be2ca636891c0eb4282c1db}, issn = {1550-4786}, keywords = {anomaly-detection time-series}, month = nov, pages = {8 pp.-}, timestamp = {2019-06-10T18:07:16.000+0200}, title = {Modeling multiple time series for anomaly detection}, url = {https://ieeexplore.ieee.org/document/1565666/}, year = 2005 }