Seglearn: A Python Package for Learning Sequences and Time Series
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Abstract (arXiv)
Seglearn is an open-source python package for machine learning time series or sequences using a sliding window segmentation approach. The implementation provides a flexible pipeline for tackling classification, regression, and forecasting problems with multivariate sequence and contextual data. This package is compatible with scikit-learn and is listed under scikit-learn Related Projects. The package depends on numpy, scipy, and scikit-learn. Seglearn is distributed under the BSD 3-Clause License. Documentation includes a detailed API description, user guide, and examples. Unit tests provide a high degree of code coverage.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Mar 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2.
5 papers.
- 2023Clairvoyance: A Pipeline Toolkit for Medical Time Seriescited by 12
- 2021tsflex: flexible time series processing & feature extractioncited by 8
- 2022HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Pythoncited by 6
- 2023TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicinecited by 5
- 2022A Review of Open Source Software Tools for Time Series Analysiscited by 0
- 2023Clairvoyance: A Pipeline Toolkit for Medical Time Series28 Oct 2023
- 2023TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine28 Jan 2023
- 2022HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python7 Jul 2022
- 2022A Review of Open Source Software Tools for Time Series Analysis10 Mar 2022
- 2021tsflex: flexible time series processing & feature extraction24 Nov 2021