{"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/ensembles-of-randomized-time-series-shapelets","title":"Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs","arxiv_id":"1702.06712","date":"2017-02-22","proceeding":null,"authors":["Atif Raza","Stefan Kramer"],"abstract":"Shapelets are discriminative time series subsequences that allow generation\nof interpretable classification models, which provide faster and generally\nbetter classification than the nearest neighbor approach. However, the shapelet\ndiscovery process requires the evaluation of all possible subsequences of all\ntime series in the training set, making it extremely computation intensive.\nConsequently, shapelet discovery for large time series datasets quickly becomes\nintractable. A number of improvements have been proposed to reduce the training\ntime. These techniques use approximation or discretization and often lead to\nreduced classification accuracy compared to the exact method.\n  We are proposing the use of ensembles of shapelet-based classifiers obtained\nusing random sampling of the shapelet candidates. Using random sampling reduces\nthe number of evaluated candidates and consequently the required computational\ncost, while the classification accuracy of the resulting models is also not\nsignificantly different than that of the exact algorithm. The combination of\nrandomized classifiers rectifies the inaccuracies of individual models because\nof the diversity of the solutions. Based on the experiments performed, it is\nshown that the proposed approach of using an ensemble of inexpensive\nclassifiers provides better classification accuracy compared to the exact\nmethod at a significantly lesser computational cost.","url_abs":"http://arxiv.org/abs/1702.06712v1","url_pdf":"http://arxiv.org/pdf/1702.06712v1.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":"ensembles-of-randomized-time-series-shapelets","repo_url":"https://github.com/atifraza/random-shapelet-ensembles","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}