{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/convolutional-shapelet-transform-a-new","title":"Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets","arxiv_id":"2109.13514","date":"2021-09-28","proceeding":null,"authors":["Antoine Guillaume","Christel Vrain","Elloumi Wael"],"abstract":"Shapelet-based algorithms are widely used for time series classification because of their ease of interpretation, but they are currently outperformed by recent state-of-the-art approaches. We present a new formulation of time series shapelets including the notion of dilation, and we introduce a new shapelet feature to enhance their discriminative power for classification. Experiments performed on 112 datasets show that our method improves on the state-of-the-art shapelet algorithm, and achieves comparable accuracy to recent state-of-the-art approaches, without sacrificing neither scalability, nor interpretability.","url_abs":"https://arxiv.org/abs/2109.13514v2","url_pdf":"https://arxiv.org/pdf/2109.13514v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"convolutional-shapelet-transform-a-new","repo_url":"https://github.com/baraline/convst","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/time-series-classification-on-acsf1","task":"Time Series Classification","dataset":"ACSF1","model":"R_DST_Ensemble","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy(30-fold)":"0.8433333333333333"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-adiac","task":"Time Series Classification","dataset":"Adiac","model":"R_DST_Ensemble","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy(30-fold)":"0.80230179028133"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-arrowhead","task":"Time Series Classification","dataset":"ArrowHead","model":"R_DST_Ensemble","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy(30-fold)":"0.8912380952380949"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-beef","task":"Time Series Classification","dataset":"Beef","model":"R_DST_Ensemble","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy(30-fold)":"0.7511111111111111"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-ecg200","task":"Time Series Classification","dataset":"ECG200","model":"R_DST_Ensemble","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy(30-fold)":"0.9016666666666667"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-ecg5000","task":"Time Series Classification","dataset":"ECG5000","model":"R_DST_Ensemble","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy(30-fold)":"0.9467629629629628"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-earthquakes","task":"Time Series Classification","dataset":"Earthquakes","model":"R_DST_Ensemble","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy(30-fold)":"0.7390887290167865"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-wafer","task":"Time Series Classification","dataset":"Wafer","model":"R_DST_Ensemble","rank_in_archive_order":1,"of":10,"metrics":{"Accuracy":"0.9999513303049968","Accuracy(30-fold)":"0.9999513303049968"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}