swMATH ID: 23641
Software Authors: David M. Burns, Cari M. Whyne
Description: Seglearn: A Python Package for Learning Sequences and Time Series. 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.
Homepage: https://github.com/dmbee/seglearn
Source Code:  https://github.com/dmbee/seglearn
Keywords: Machine Learning; arXiv_stat.ML; Learning; arXiv_cs.LG; arXiv_publication; Time-Series; Sequences; Python
Related Software: Python; tslearn; tsfresh; SciPy; Scikit; Statsmodels; GitHub; pyts; sktime; NumPy; cesium; hmmlearn; STUMPY; Numba; UCR Suite; seg1d; WESAD; TSFEL; Kats; pandas
Cited in: 0 Documents

Standard Articles

1 Publication describing the Software Year
Seglearn: A Python Package for Learning Sequences and Time Series arXiv
David M. Burns, Cari M. Whyne