{"id":102,"date":"2022-07-14T11:45:47","date_gmt":"2022-07-14T09:45:47","guid":{"rendered":"https:\/\/project.inria.fr\/aaltd22\/?page_id=102"},"modified":"2022-09-30T17:53:21","modified_gmt":"2022-09-30T15:53:21","slug":"accepted-papers","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/aaltd22\/accepted-papers\/","title":{"rendered":"Accepted papers"},"content":{"rendered":"<h3>Oral presentation<\/h3>\n<ul>\n<li>Arik Ermshaus, Patrick Sch\u00e4fer and Ulf Leser. Window Size Selection In Unsupervised Time Series Analytics: A Review and Benchmark <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_3876.pdf\">PDF<\/a><\/li>\n<li>Sepideh Koohfar and Laura Dietz. Adjustable Context-aware Transformer <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_4854.pdf\">PDF<\/a><\/li>\n<li>Thach Le Nguyen and Georgiana Ifrim. Fast Time Series Classification with Random Symbolic Subsequences <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_5778.pdf\">PDF<\/a><\/li>\n<li>\u00c1ngel L\u00f3pez-Oriona, Pablo Montero-Manso and Jos\u00e9 Vilar. Time series clustering based on prediction accuracy of global forecasting model <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_2854.pdf\">PDF<\/a><\/li>\n<li>Michael Franklin Mbouopda, Thomas Guyet, Nicolas Labroche and Abel Heuriot. Local vs global forecasting methods for groundwater level prediction <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_7327.pdf\">PDF<\/a><\/li>\n<li>Naji Najari, Samuel Berlemont, Gregoire Lefebvre, Stefan Duffner and Christophe Garcia. RESIST: Robust Transformer for Unsupervised Time Series Anomaly Detection <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_4615.pdf\">PDF<\/a><\/li>\n<\/ul>\n<h3>Poster presentation<\/h3>\n<ul>\n<li>Chao Chen and Anuj Srivastava. ElasticRegNet: An Unsupervised Network for Joint Temporal Alignment <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_8776.pdf\">PDF<\/a><\/li>\n<li>Oshana Dissanayake, Sarah McPherson, Emer Kennedy, Katie Sugrue, Muireann Conneely, Laurence Shalloo, P\u00e1draig Cunningham and Lucile Riaboff. Identification of the Best Accelerometer Features and Time-scale to Detect Disturbances in Calves <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_0177.pdf\">PDF<\/a><\/li>\n<li>Nathan Morsa. EDGAR: Embedded Detection of Gunshots by AI in Real-time <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_6574.pdf\">PDF<\/a><\/li>\n<li>Neha Pant, Durga Toshniwal and Bhola Ram Gurjar. Application of Attention Mechanism combined with Long Short-Term Memory for forecasting Dissolved Oxygen in Ganga River flowing through Uttar Pradesh, India <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_6615.pdf\">PDF<\/a><\/li>\n<li>Alejandro Pasos Ruiz and Anthony Bagnall. Dimension selection strategies for multivariate time series classification with HIVE-COTEv2.0 <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_4258.pdf\">PDF<\/a><\/li>\n<li>Gautier Pialla, Maxime Devanne, Jonathan Weber, Lhassane Idoumghar and Germain Forestier. Data Augmentation for Deep Learning Time Series Classification <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_4949.pdf\">PDF<\/a><\/li>\n<li>Niccol\u00f2 Zangrando, Piero Fraternali, Rocio Nahime Torres, Marco Petri, Nicol\u00f2 Oreste Pinciroli Vago and Sergio Herrera. A framework supporting the life-cycle of time series anomaly detection applications <a href=\"https:\/\/project.inria.fr\/aaltd22\/files\/2022\/08\/AALTD22_paper_3710.pdf\">PDF<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Oral presentation Arik Ermshaus, Patrick Sch\u00e4fer and Ulf Leser. Window Size Selection In Unsupervised Time Series Analytics: A Review and Benchmark PDF Sepideh Koohfar and Laura Dietz. Adjustable Context-aware Transformer PDF Thach Le Nguyen and Georgiana Ifrim. Fast Time Series Classification with Random Symbolic Subsequences PDF \u00c1ngel L\u00f3pez-Oriona, Pablo Montero-Manso\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/aaltd22\/accepted-papers\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":733,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-102","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/aaltd22\/wp-json\/wp\/v2\/pages\/102","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/aaltd22\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/aaltd22\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/aaltd22\/wp-json\/wp\/v2\/users\/733"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/aaltd22\/wp-json\/wp\/v2\/comments?post=102"}],"version-history":[{"count":5,"href":"https:\/\/project.inria.fr\/aaltd22\/wp-json\/wp\/v2\/pages\/102\/revisions"}],"predecessor-version":[{"id":164,"href":"https:\/\/project.inria.fr\/aaltd22\/wp-json\/wp\/v2\/pages\/102\/revisions\/164"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/aaltd22\/wp-json\/wp\/v2\/media?parent=102"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}