Anomaly Detection Paradigm for Multivariate Time Series Data Mining for Healthcare
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Department of Electical and Computer Engineering, University of ArizonaIssue Date
2022
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Razaque, A., Abenova, M., Alotaibi, M., Alotaibi, B., Alshammari, H., Hariri, S., & Alotaibi, A. (2022). Anomaly Detection Paradigm for Multivariate Time Series Data Mining for Healthcare. Applied Sciences (Switzerland), 12(17).Journal
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Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Time series data are significant, and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introduce an anomaly detection paradigm called novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data in the healthcare. The proposed paradigm inherits the features from two state-of-the-art algorithms: Scalable Time series Anytime Matrix Profile (STAMP) and Scalable Time-series Ordered-search Matrix Profile (STOMP). The proposed NMP caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP can be used on large multivariate data sets and generates approximate solutions of high quality in a reasonable time. It is implemented on a Python platform. To determine its effectiveness, it is compared with the state-of-the-art matrix profile algorithms, i.e., STAMP and STOMP. The results confirm that the proposed NMP provides higher accuracy than the compared algorithms. © 2022 by the authors.Note
Open access journalISSN
2076-3417Version
Final published versionae974a485f413a2113503eed53cd6c53
10.3390/app12178902
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Except where otherwise noted, this item's license is described as Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).