{"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/seglearn-a-python-package-for-learning","title":"Seglearn: A Python Package for Learning Sequences and Time Series","arxiv_id":"1803.08118","date":"2018-03-21","proceeding":null,"authors":["David M. Burns","Cari M. Whyne"],"abstract":"Seglearn is an open-source python package for machine learning time series or\nsequences using a sliding window segmentation approach. The implementation\nprovides a flexible pipeline for tackling classification, regression, and\nforecasting problems with multivariate sequence and contextual data. This\npackage is compatible with scikit-learn and is listed under scikit-learn\nRelated Projects. The package depends on numpy, scipy, and scikit-learn.\nSeglearn is distributed under the BSD 3-Clause License. Documentation includes\na detailed API description, user guide, and examples. Unit tests provide a high\ndegree of code coverage.","url_abs":"http://arxiv.org/abs/1803.08118v3","url_pdf":"http://arxiv.org/pdf/1803.08118v3.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":"seglearn-a-python-package-for-learning","repo_url":"https://github.com/dmbee/seglearn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"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":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}