{"id":592,"date":"2020-09-21T17:15:15","date_gmt":"2020-09-21T17:15:15","guid":{"rendered":"https:\/\/eic.cefet-rj.br\/~dal\/?page_id=592"},"modified":"2025-10-21T00:44:19","modified_gmt":"2025-10-21T00:44:19","slug":"tspred-package-for-r-functions-for-benchmarking-time-series-prediction","status":"publish","type":"post","link":"https:\/\/eic.cefet-rj.br\/~dal\/tspred-package-for-r-functions-for-benchmarking-time-series-prediction\/","title":{"rendered":"TSPred Package for R: Functions for Benchmarking Time Series Prediction"},"content":{"rendered":"<p><img decoding=\"async\" class=\"size-full wp-image-1783 alignleft\" src=\"https:\/\/eic.cefet-rj.br\/~dal\/wp-content\/uploads\/2023\/03\/logo.png\" alt=\"\" width=\"125\" height=\"125\" \/>Functions for time series prediction and accuracy assessment using automatic linear modeling. The generated linear models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.<\/p>\n<p><strong>Available at CRAN:<\/strong> <a href=\"https:\/\/CRAN.R-project.org\/package=TSPred\">https:\/\/CRAN.R-project.org\/package=TSPred<\/a><\/p>\n<p><strong>Code repository at Git-Hub<\/strong>: <a href=\"https:\/\/github.com\/RebeccaSalles\/TSPred\">https:\/\/github.com\/RebeccaSalles\/TSPred<\/a><\/p>\n<p><strong>Reference manual:<\/strong> <a href=\"http:\/\/cran.r-project.org\/web\/packages\/TSPred\/TSPred.pdf\">TSPred.pdf<\/a><\/p>\n<p><strong>Acknowledgments:<\/strong> The authors thank CNPq for partially sponsoring this work.<\/p>\n<p><strong>Team:<\/strong> Rebecca Pontes Salles (<a href=\"mailto:rebeccapsalles@acm.org\">rebeccapsalles@acm.org<\/a>) and Eduardo Ogasawara (<a href=\"mailto:eogasawara@ieee.org\">eogasawara@ieee.org<\/a>)<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Functions for time series prediction and accuracy assessment using automatic linear modeling. The generated linear models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used. Available at [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[],"class_list":["post-592","post","type-post","status-publish","format-standard","hentry","category-artefatos","entry"],"_links":{"self":[{"href":"https:\/\/eic.cefet-rj.br\/~dal\/wp-json\/wp\/v2\/posts\/592","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/eic.cefet-rj.br\/~dal\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/eic.cefet-rj.br\/~dal\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~dal\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~dal\/wp-json\/wp\/v2\/comments?post=592"}],"version-history":[{"count":3,"href":"https:\/\/eic.cefet-rj.br\/~dal\/wp-json\/wp\/v2\/posts\/592\/revisions"}],"predecessor-version":[{"id":1788,"href":"https:\/\/eic.cefet-rj.br\/~dal\/wp-json\/wp\/v2\/posts\/592\/revisions\/1788"}],"wp:attachment":[{"href":"https:\/\/eic.cefet-rj.br\/~dal\/wp-json\/wp\/v2\/media?parent=592"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~dal\/wp-json\/wp\/v2\/categories?post=592"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~dal\/wp-json\/wp\/v2\/tags?post=592"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}