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Multivariate time series classification with temporal abstractions

Batal, L and Sacchi, L and Bellazzi, R and Hauskrecht, M (2009) Multivariate time series classification with temporal abstractions. In: UNSPECIFIED.

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The increase in the number of complex temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data. This work focuses on methods for multivariate time-series classification. Time series classification is a challenging problem mostly because the number of temporal features that describe the data and are potentially useful for classification is enormous. We study and develop a temporal abstraction framework for generating multivariate time series features suitable for classification tasks. We propose the STF-Mine algorithm that automatically mines discriminative temporal abstraction patterns from the time series data and uses them to learn a classification model. Our experimental evaluations, carried out on both synthetic and real world medical data, demonstrate the benefit of our approach in learning accurate classifiers for time-series datasets. Copyright © 2009, Assocation for the Advancement of ArtdicaI Intelligence ( All rights reserved.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Batal, L
Sacchi, L
Bellazzi, R
Hauskrecht, Mmilos@pitt.eduMILOS
Date: 4 November 2009
Date Type: Publication
Journal or Publication Title: Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22
Page Range: 344 - 349
Event Type: Conference
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Refereed: No
ISBN: 9781577354192
Date Deposited: 13 Jan 2010 18:15
Last Modified: 02 Feb 2019 16:55


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