Learning Comprehensible Descriptions of Multivariate Time Series

Abstract

Supervised classification is one of the most active areas of machine learning research. Most work has focused on classification in static domains, where an instantaneous snapshot of attributes is meaningful. In many domains, attributes are not static; in fact, it is the way they vary temporally that can make classification possible. Examples of such domains include speech recognition, gesture recognition and electrocardiograph classification. While it is possible to use ad-hoc, domain-specific techniques for "attening" the time series to a learner-friendly representation, this fails to take into account both the special problems and special heuristics applicable to temporal data and often results in unreadable concept descriptions. Though traditional time series techniques can sometimes produce accurate classi ers, few can provide comprehensible descriptions. We propose a general architecture for classification and description of multivariate time series. It employs event primitives to ana...