{"id":2723,"date":"2026-04-04T12:04:09","date_gmt":"2026-04-04T15:04:09","guid":{"rendered":"https:\/\/eic.cefet-rj.br\/~eogasawara\/?page_id=2723"},"modified":"2026-08-24T05:53:52","modified_gmt":"2026-08-24T08:53:52","slug":"tspredit-2","status":"publish","type":"page","link":"https:\/\/eic.cefet-rj.br\/~eogasawara\/en\/tspredit-2\/","title":{"rendered":"TSPredIT"},"content":{"rendered":"<p><strong>TSPredIT<\/strong> is an R framework for time series forecasting with integrated tuning. The package organizes the predictive process as a modular pipeline involving data representation, temporal splitting, filtering, augmentation, normalization, modeling, comparison, and hyperparameter tuning.<\/p>\n<p>This material presents <code>tspredit<\/code> as a support resource for studies and experiments in time series forecasting, with examples focused on building and evaluating predictive workflows.<\/p>\n<h3><strong>Supporting material<\/strong><\/h3>\n<ul>\n<li><a href=\"https:\/\/github.com\/cefet-rj-dal\/tspredit\/wiki\">Wiki with documentation, examples, and package materials<\/a><\/li>\n<\/ul>\n<h3><strong>Tutorial playlist<br \/>\n<\/strong><\/h3>\n<h3><strong><a href=\"https:\/\/www.youtube.com\/playlist?list=PLJb2qK1RWkbGlxUAljn-9eP2r_3m70aUC\" target=\"_blank\" rel=\"nofollow noopener\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone\" src=\"https:\/\/camo.githubusercontent.com\/be5f7579bfbd708b96135537d8491b8d1e8ebf810734281595411d95a11b5cd4\/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d5761746368253230706c61796c6973742d7265643f6c6f676f3d796f7574756265266c6f676f436f6c6f723d7768697465\" alt=\"Watch the playlist on YouTube\" width=\"161\" height=\"20\" data-canonical-src=\"https:\/\/img.shields.io\/badge\/YouTube-Watch%20playlist-red?logo=youtube&amp;logoColor=white\" \/><\/a><\/strong><\/h3>\n<h3><strong>\u00a0<\/strong><\/h3>\n","protected":false},"excerpt":{"rendered":"<p>TSPredIT is an R framework for time series forecasting with integrated tuning. The package organizes the predictive process as a modular pipeline involving data representation, temporal splitting, filtering, augmentation, normalization, modeling, comparison, and hyperparameter tuning. This material presents tspredit as a support resource for studies and experiments in time series forecasting, with examples focused on [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2723","page","type-page","status-publish","hentry","entry"],"_links":{"self":[{"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/pages\/2723","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/comments?post=2723"}],"version-history":[{"count":5,"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/pages\/2723\/revisions"}],"predecessor-version":[{"id":2798,"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/pages\/2723\/revisions\/2798"}],"wp:attachment":[{"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/media?parent=2723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}