{"id":1699,"date":"2024-09-17T21:03:22","date_gmt":"2024-09-17T21:03:22","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1699"},"modified":"2024-09-17T21:06:08","modified_gmt":"2024-09-17T21:06:08","slug":"vol22-no1-art3","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/","title":{"rendered":"Blending Ensemble applied to Open-Set Recognition for Time Series Classification"},"content":{"rendered":"<p>Leonardo de Marqui Marques <a href=\"https:\/\/orcid.org\/0009-0009-6590-6149\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Andr\u00e9 Eugenio Lazzaretti <a href=\"https:\/\/orcid.org\/0000-0003-1861-3369\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, &amp; Heitor Silv\u00e9rio Lopes <a href=\"https:\/\/orcid.org\/0000-0003-3984-1432\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> Time Series (TS), including stock prices, temperatures, and health markers are monitored all the time and everywhere. Time-dependent events, and TS are frequently studied in the scope of forecasting, where past values of the TS are used to preview future ones. On the other hand, Time Series Classification (TSC) aims at creating models that label TS instances. Open Set Recognition (OSR) techniques, designed to classify known samples and detect unknowns simultaneously, have been less applied in TSC compared to other fields like image classification. Existing methods have limitations, such as not using known-unknown in the training stage, limited transfer learning across Neural Network models, and experiments with benchmark datasets with narrow coverage. This study introduces a novel OSR approach for TSC by addressing the mentioned issues and using a blending ensemble of Neural Networks with an OpenMax layer. The results vouch for the model\u2019s performance and potential superiority as an alternative to existing methods in open-set recognition tasks.<\/p>\n<p><strong>Keywords:<\/strong> Time Series Classification, Open Set Recognition, Machine Learning.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol22-no1-art3\">10.21528\/lnlm-vol22-no1-art3<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2024\/12\/vol22-no1-art3.pdf\">vol22-no1-art3.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2024\/12\/vol22-no1-art3.bib\">vol22-no1-art3.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Leonardo de Marqui Marques , Andr\u00e9 Eugenio Lazzaretti , &amp; Heitor Silv\u00e9rio Lopes Abstract: Time Series (TS), including stock prices, temperatures, and health markers are monitored all the time and everywhere. Time-dependent events, and TS are frequently studied in the <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1675,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1699","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Blending Ensemble applied to Open-Set Recognition for Time Series Classification - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Blending Ensemble applied to Open-Set Recognition for Time Series Classification - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Leonardo de Marqui Marques , Andr\u00e9 Eugenio Lazzaretti , &amp; Heitor Silv\u00e9rio Lopes Abstract: Time Series (TS), including stock prices, temperatures, and health markers are monitored all the time and everywhere. Time-dependent events, and TS are frequently studied in the Read More ...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/\" \/>\n<meta property=\"og:site_name\" content=\"Learning and NonLinear Models\" \/>\n<meta property=\"article:modified_time\" content=\"2024-09-17T21:06:08+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/\",\"name\":\"Blending Ensemble applied to Open-Set Recognition for Time Series Classification - Learning and NonLinear Models\",\"isPartOf\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"datePublished\":\"2024-09-17T21:03:22+00:00\",\"dateModified\":\"2024-09-17T21:06:08+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/#primaryimage\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Browse issues\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 22 &#8211; N\u00famero 1\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Blending Ensemble applied to Open-Set Recognition for Time Series Classification\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"name\":\"Learning and NonLinear Models\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"pt-BR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\",\"name\":\"Learning and NonLinear Models\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"width\":398,\"height\":94,\"caption\":\"Learning and NonLinear Models\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Blending Ensemble applied to Open-Set Recognition for Time Series Classification - Learning and NonLinear Models","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no1\/vol22-no1-art3\/","og_locale":"pt_BR","og_type":"article","og_title":"Blending Ensemble applied to Open-Set Recognition for Time Series Classification - Learning and NonLinear Models","og_description":"Leonardo de Marqui Marques , Andr\u00e9 Eugenio Lazzaretti , &amp; Heitor Silv\u00e9rio Lopes Abstract: Time Series (TS), including stock prices, temperatures, and health markers are monitored all the time and everywhere. 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