{"id":1920,"date":"2024-08-16T17:08:19","date_gmt":"2024-08-16T20:08:19","guid":{"rendered":"https:\/\/eic.cefet-rj.br\/~eogasawara\/?p=1920"},"modified":"2025-12-02T14:19:48","modified_gmt":"2025-12-02T17:19:48","slug":"harbinger-paper","status":"publish","type":"post","link":"https:\/\/eic.cefet-rj.br\/~eogasawara\/harbinger-paper\/","title":{"rendered":"harbinger: A Unified Time Series Event Detection Framework"},"content":{"rendered":"<div class=\"elementor-element elementor-element-7bdb3a45 elementor-widget elementor-widget-text-editor\" data-id=\"7bdb3a45\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<div class=\"elementor-text-editor elementor-clearfix\">\n<p>By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) &lt;<a href=\"https:\/\/doi.org\/10.5753%2Fsbbd.2020.13626\" target=\"_top\" rel=\"noopener\">doi:10.5753\/sbbd.2020.13626<\/a>&gt;.<\/p>\n<p><strong>Available at CRAN<\/strong>: <a href=\"https:\/\/CRAN.R-project.org\/package=harbinger\">https:\/\/CRAN.R-project.org\/package=harbinger<\/a><\/p>\n<p><strong>Code repository at Git-Hub<\/strong>: <a href=\"https:\/\/github.com\/cefet-rj-dal\/harbinger\">https:\/\/github.com\/cefet-rj-dal\/harbinger<\/a><\/p>\n<p>Slide: <a href=\"https:\/\/github.com\/eogasawara\/packages\/blob\/main\/daltoolbox.pdf\">daltoolbox.pdf<\/a><\/p>\n<p class=\"responsive-video-wrap clr\"><iframe loading=\"lazy\" title=\"DAL Toolbox: Leveraging Experiment Lines for Reproducible Data Analytics\" width=\"1200\" height=\"675\" src=\"https:\/\/www.youtube.com\/embed\/nfaMicE7kIc?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[17],"tags":[],"class_list":["post-1920","post","type-post","status-publish","format-standard","hentry","category-artefatos","entry"],"_links":{"self":[{"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/posts\/1920","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/types\/post"}],"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=1920"}],"version-history":[{"count":7,"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/posts\/1920\/revisions"}],"predecessor-version":[{"id":2554,"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/posts\/1920\/revisions\/2554"}],"wp:attachment":[{"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/media?parent=1920"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/categories?post=1920"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/eic.cefet-rj.br\/~eogasawara\/wp-json\/wp\/v2\/tags?post=1920"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}