Finding the most unusual time series subsequence: algorithms and applications
- 23 November 2006
- journal article
- Published by Springer Science and Business Media LLC in Knowledge and Information Systems
- Vol. 11 (1), 1-27
- https://doi.org/10.1007/s10115-006-0034-6
Abstract
No abstract availableKeywords
This publication has 13 references indexed in Scilit:
- Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series DatabasesPublished by Society for Industrial & Applied Mathematics (SIAM) ,2005
- Visually mining and monitoring massive time seriesPublished by Association for Computing Machinery (ACM) ,2004
- Towards parameter-free data miningPublished by Association for Computing Machinery (ACM) ,2004
- Online novelty detection on temporal sequencesPublished by Association for Computing Machinery (ACM) ,2003
- A symbolic representation of time series, with implications for streaming algorithmsPublished by Association for Computing Machinery (ACM) ,2003
- Distinguishing string selection problemsInformation and Computation, 2003
- Finding recurrent sources in sequencesPublished by Association for Computing Machinery (ACM) ,2003
- On Complementarity of Cluster and Outlier Detection SchemesLecture Notes in Computer Science, 2003
- On the need for time series data mining benchmarksPublished by Association for Computing Machinery (ACM) ,2002
- Distance-based outliers: algorithms and applicationsThe VLDB Journal, 2000