On the adoption of abductive reasoning for time series interpretation
  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.
