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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Abstract

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 (www.aaai.org). All rights reserved.

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Details

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Status:	Published
Creators/Authors:	Creators Email Pitt Username ORCID Batal, L Sacchi, L Bellazzi, R Hauskrecht, M milos@pitt.edu MILOS
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
URI:	http://d-scholarship.pitt.edu/id/eprint/2796

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