{"id":933,"date":"2018-09-03T16:31:51","date_gmt":"2018-09-03T15:31:51","guid":{"rendered":"http:\/\/babel.isa.uma.es\/kipr\/?p=933"},"modified":"2018-09-03T16:31:51","modified_gmt":"2018-09-03T15:31:51","slug":"interpreting-time-series-patterns-through-reasoning","status":"publish","type":"post","link":"https:\/\/babel.isa.uma.es\/kipr\/?p=933","title":{"rendered":"Interpreting time series patterns through reasoning"},"content":{"rendered":"<h4>T. Teijeiro, P. F\u00e9lix, <strong>On the adoption of abductive reasoning for time series interpretation<\/strong>, Artificial Intelligence, Volume 262, 2018, Pages 163-188, <a href=\"https:\/\/doi.org\/10.1016\/j.artint.2018.06.005\" target=\"_blank\">DOI: 10.1016\/j.artint.2018.06.005<\/a>.<\/h4>\n<blockquote><p>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.<\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>T. Teijeiro, P. F\u00e9lix, On the adoption of abductive reasoning for time series interpretation, Artificial Intelligence, Volume 262, 2018, Pages <span class=\"ellipsis\">&hellip;<\/span> <span class=\"more-link-wrap\"><a href=\"https:\/\/babel.isa.uma.es\/kipr\/?p=933\" class=\"more-link\"><span>Read More &rarr;<\/span><\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[37],"tags":[361,47],"class_list":["post-933","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","tag-abductive-reasoning","tag-time-series-analysis"],"_links":{"self":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/933"}],"collection":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=933"}],"version-history":[{"count":1,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/933\/revisions"}],"predecessor-version":[{"id":934,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/933\/revisions\/934"}],"wp:attachment":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=933"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=933"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=933"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}