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 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 patterns appearing in a time series. The result of the 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 promising application domain, the interpretation of the electrocardiogram allows us to highlight the strengths of the present approach in comparison with traditional classification-based approaches.
from cs.AI updates on arXiv.org http://ift.tt/2cZLgRn
via IFTTT
No comments:
Post a Comment