{"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/trainable-time-warping-aligning-time-series","title":"Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain","arxiv_id":"1903.09245","date":"2019-03-21","proceeding":null,"authors":["Soheil Khorram","Melvin G McInnis","Emily Mower Provost"],"abstract":"DTW calculates the similarity or alignment between two signals, subject to\ntemporal warping. However, its computational complexity grows exponentially\nwith the number of time-series. Although there have been algorithms developed\nthat are linear in the number of time-series, they are generally quadratic in\ntime-series length. The exception is generalized time warping (GTW), which has\nlinear computational cost. Yet, it can only identify simple time warping\nfunctions. There is a need for a new fast, high-quality multisequence alignment\nalgorithm. We introduce trainable time warping (TTW), whose complexity is\nlinear in both the number and the length of time-series. TTW performs alignment\nin the continuous-time domain using a sinc convolutional kernel and a\ngradient-based optimization technique. We compare TTW and GTW on 85 UCR\ndatasets in time-series averaging and classification. TTW outperforms GTW on\n67.1% of the datasets for the averaging tasks, and 61.2% of the datasets for\nthe classification tasks.","url_abs":"http://arxiv.org/abs/1903.09245v1","url_pdf":"http://arxiv.org/pdf/1903.09245v1.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":"trainable-time-warping-aligning-time-series","repo_url":"https://github.com/soheil-khorram/TTW","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"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"},{"task_slug":"time-series-averaging","task_name":"Time Series Averaging"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1903.09245","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}