AAAI Publications, Twenty-Second International FLAIRS Conference

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Multivariate Time Series Classification with Temporal Abstractions
Iyad Batal, Lucia Sacchi, Riccardo Bellazzi, Milos Hauskrecht

Last modified: 2009-03-17

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.

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