{"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/neuralwarp-time-series-similarity-with","title":"NeuralWarp: Time-Series Similarity with Warping Networks","arxiv_id":"1812.08306","date":"2018-12-20","proceeding":null,"authors":["Josif Grabocka","Lars Schmidt-Thieme"],"abstract":"Research on time-series similarity measures has emphasized the need for\nelastic methods which align the indices of pairs of time series and a plethora\nof non-parametric have been proposed for the task. On the other hand, deep\nlearning approaches are dominant in closely related domains, such as learning\nimage and text sentence similarity. In this paper, we propose\n\\textit{NeuralWarp}, a novel measure that models the alignment of time-series\nindices in a deep representation space, by modeling a warping function as an\nupper level neural network between deeply-encoded time series values.\nExperimental results demonstrate that \\textit{NeuralWarp} outperforms both\nnon-parametric and un-warped deep models on a range of diverse real-life\ndatasets.","url_abs":"http://arxiv.org/abs/1812.08306v1","url_pdf":"http://arxiv.org/pdf/1812.08306v1.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":"neuralwarp-time-series-similarity-with","repo_url":"https://github.com/josifgrabocka/neuralwarp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"neuralwarp-time-series-similarity-with","repo_url":"https://github.com/fabriciomurai/neuralwarp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-similarity","task_name":"Sentence Similarity"},{"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":"https://app.syntology.ai/?focus=1812.08306","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}