{"id":109,"date":"2018-08-01T13:38:10","date_gmt":"2018-08-01T11:38:10","guid":{"rendered":"https:\/\/project.inria.fr\/aaldt18\/?page_id=109"},"modified":"2018-08-13T10:02:14","modified_gmt":"2018-08-13T08:02:14","slug":"accepted-papers","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/aaldt18\/accepted-papers\/","title":{"rendered":"Accepted papers"},"content":{"rendered":"<p>List of accepted papers<\/p>\n<p>Accepted for oral presentation:<\/p>\n<ul>\n<li>Patrick Sch\u00e4fer and Ulf Leser. <strong>Multivariate Time Series Classification with WEASEL+MUSE<\/strong><\/li>\n<li>Aaron Bostrom and Anthony Bagnall. <strong>A shapelet transform for multivariate time series classification<\/strong><\/li>\n<li>Simon Rabinowicz, Raphaela Butz, Arjen Hommersom and Matt Williams. <strong>CSBN: A Hybrid Approach For Survival Time Prediction With Missing Data<\/strong><\/li>\n<li>James Large, Paul Southam and Anthony Bagnall.<strong> Can automated smoothing significantly improve benchmark time series classification algorithms?<\/strong><\/li>\n<li>Colin Leverger, Vincent Lemaire, Simon Malinowski, Thomas Guyet and Laurence Roz\u00e9, <strong>Day-ahead time series forecasting: application to capacity planning<\/strong><\/li>\n<li>Alexandre Sahuguede, Euriell Le Corronc and Marie-V\u00e9ronique Le Lann. <strong>An Ordered Chronicle Discovery Algorithm<\/strong><\/li>\n<li>Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar and Pierre-Alain Muller. <strong>Data augmentation using synthetic data for time series classification with deep residual networks<\/strong><\/li>\n<li>Junning Deng, Jefrey Lijffijt, Bo Kang and Tijl De Bie. <strong>Subjectively Interesting Motifs in Time Series<\/strong><\/li>\n<li>Kun Tu, Jian Li, Don Towsley, Dave Braines and Liam Turner. <strong>Network Classification in Temporal Networks Using Motifs<\/strong><\/li>\n<li>Denis Smirnov and Engelbert Mephu Nguifo. <strong>Time Series Classification with Recurrent Neural Networks<\/strong><\/li>\n<\/ul>\n<p>Accepted for poster presentation:<\/p>\n<ul>\n<li>Hugo Hromic and Conor Hayes. <strong>Visualising the Evolution of Dynamic Communities in Social Networks using Timelines<\/strong><\/li>\n<li>Vivek Mahato and Padraig Cunningham. <strong>A Case-Study on the Impact of Dynamic Time Warping in Time Series Regression<\/strong><\/li>\n<li>Babak Hosseini and Barbara Hammer. <strong>Multiple-Kernel Dictionary Learning for Sparse Reconstruction of Unseen Multivariate Time-series<\/strong><\/li>\n<li>Aakanksha Bapna, Naveen Thokala, Kriti Kumar and M. Girish Chandra. <strong>On the Feasibility of Deep Belief Networks for Tool Wear Monitoring in CNC Machines<\/strong><\/li>\n<li>Michael Flynn, Jason Lines and Anthony Bagnall. <strong>c-RISE: contract random interval spectral ensemble for time series classification<\/strong><\/li>\n<li>Jason Lines. <strong>On Setting Parameters with Elastic Distance Measures for Time Series Classification: A Practical Case Study with Dynamic Time Warping<\/strong><\/li>\n<li>Dominique Gay and Vincent Lemaire. <strong>Should we reload Time Series Classification Performance Evaluation ? (a position paper)<\/strong><\/li>\n<li>Lejeail Pierre, Vincent Lemaire, Antoine Cornu\u00e9jols and Adam Ouorou. <strong>TriClustering based outlier-shape score for time series in a fraud detection platform<\/strong><\/li>\n<li>Petr Pulc, Oliver Keru\u013e-Kmec, Tom\u00e1\u0161 \u0160abata and Martin Holena. <strong>Motion Segmentation by Semi-Supervised Classification in Dynamic Scenery<\/strong><\/li>\n<li>Eman Awad and Fintan Costello. <strong>Learning from temporal data: feature formation and prediction for categorical time series<\/strong><\/li>\n<li>Manel Chehibi and Aymen Gammoudi. <strong>An Intelligent Approach for Managing Uncertainty in Temporal Databases: First Steps<\/strong><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>List of accepted papers Accepted for oral presentation: Patrick Sch\u00e4fer and Ulf Leser. Multivariate Time Series Classification with WEASEL+MUSE Aaron Bostrom and Anthony Bagnall. A shapelet transform for multivariate time series classification Simon Rabinowicz, Raphaela Butz, Arjen Hommersom and Matt Williams. CSBN: A Hybrid Approach For Survival Time Prediction With\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/aaldt18\/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-109","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/aaldt18\/wp-json\/wp\/v2\/pages\/109","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/aaldt18\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/aaldt18\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/aaldt18\/wp-json\/wp\/v2\/users\/733"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/aaldt18\/wp-json\/wp\/v2\/comments?post=109"}],"version-history":[{"count":3,"href":"https:\/\/project.inria.fr\/aaldt18\/wp-json\/wp\/v2\/pages\/109\/revisions"}],"predecessor-version":[{"id":124,"href":"https:\/\/project.inria.fr\/aaldt18\/wp-json\/wp\/v2\/pages\/109\/revisions\/124"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/aaldt18\/wp-json\/wp\/v2\/media?parent=109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}