{"id":116,"date":"2022-05-24T18:39:45","date_gmt":"2022-05-24T18:39:45","guid":{"rendered":"https:\/\/ozer.gt\/log\/?p=116"},"modified":"2024-01-27T06:58:22","modified_gmt":"2024-01-27T06:58:22","slug":"neuralprophet-puts-facebooks-prophet-on-steroids-using-neural-networks","status":"publish","type":"post","link":"https:\/\/ozer.gt\/log\/2022\/05\/24\/neuralprophet-puts-facebooks-prophet-on-steroids-using-neural-networks\/","title":{"rendered":"NeuralProphet puts Facebook&#8217;s Prophet on steroids using neural networks"},"content":{"rendered":"<p>Models remain interpretable to the extent that the components of the original function are retained. The authors claim 55% to 92% improvement in accuracy in short to medium-term forecasts, which is impressive if generalizable. Model training time increases 4-fold but prediction time improves 14-fold. Developed on PyTorch so it can be parallelized and deployed on GPUs, potentially to reduce training time. Ported to R but using a Python environment.<\/p>\n<p>Looks promising especially for &#8220;AI on the edge&#8221; type mobile applications.<\/p>\n<p><a href=\"https:\/\/github.com\/ourownstory\/neural_prophet\">Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Models remain interpretable to the extent that the components of the original function are retained. The authors claim 55% to 92% improvement in accuracy in short to medium-term forecasts, which is impressive if generalizable. Model training time increases 4-fold but prediction time improves 14-fold. Developed on PyTorch so it can be parallelized and deployed on [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cybocfi_hide_featured_image":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-116","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts\/116","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/comments?post=116"}],"version-history":[{"count":2,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts\/116\/revisions"}],"predecessor-version":[{"id":126,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts\/116\/revisions\/126"}],"wp:attachment":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/media?parent=116"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/categories?post=116"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/tags?post=116"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}