{"id":901,"date":"2024-12-31T07:03:48","date_gmt":"2024-12-31T07:03:48","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2024\/12\/31\/2412-19897\/"},"modified":"2024-12-31T07:03:48","modified_gmt":"2024-12-31T07:03:48","slug":"2412-19897","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2024\/12\/31\/2412-19897\/","title":{"rendered":"Surrogate Modeling for Explainable Predictive Time Series Corrections"},"content":{"rendered":"<p>    Surrogate Modeling for Explainable Predictive Time Series Corrections<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2412.19897v1 Announce Type: new<br \/>\nAbstract: We introduce a local surrogate approach for explainable time-series forecasting. An initially non-interpretable predictive model to improve the forecast of a classical time-series &#8216;base model&#8217; is used. &#8216;Explainability&#8217; of the correction is provided by fitting the base model again to the data from which the error prediction is removed (subtracted), yielding a difference in the model parameters which can be interpreted. We provide illustrative examples to demonstrate the potential of the method to discover and explain underlying patterns in the data.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Alfredo Lopez, Florian Sobieczky<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2412.19897\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Surrogate Modeling for Explainable Predictive Time Series Corrections arXiv:2412.19897v1 Announce Type: new Abstract: We introduce a local surrogate approach for explainable time-series forecasting. An initially non-interpretable predictive model to improve the forecast of a classical time-series &#8216;base model&#8217; is used. &#8216;Explainability&#8217; of the correction is provided by fitting the base model again to the data [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,112],"tags":[103,325,15],"class_list":["post-901","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-model","tag-series","tag-time"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/901"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=901"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/901\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=901"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=901"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=901"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}