  Time series interpretation aims to provide an explanation of what is observed
in terms of its underlying processes. The present work is based on the
assumption that the common classification-based approaches to time series
interpretation suffer from a set of inherent weaknesses, whose ultimate cause
lies in the monotonic nature of the deductive reasoning paradigm. In this
document we propose a new approach to this problem, based on the initial
hypothesis that abductive reasoning properly accounts for the human ability to
identify and characterize the patterns appearing in a time series. The result
of this interpretation is a set of conjectures in the form of observations,
organized into an abstraction hierarchy and explaining what has been observed.
A knowledge-based framework and a set of algorithms for the interpretation task
are provided, implementing a hypothesize-and-test cycle guided by an
attentional mechanism. As a representative application domain, interpretation
of the electrocardiogram allows us to highlight the strengths of the proposed
approach in comparison with traditional classification-based approaches.
