Seglearn 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; Statsmodels; tsfresh; SciPy; Scikit; pyts; sktime; GitHub; NumPy; cesium; hmmlearn; STUMPY; Numba; Kats; Viztracer; tsflex; WESAD; TSFEL; pandas Cited in: 1 Publication Standard Articles 1 Publication describing the Software Year Seglearn: A Python Package for Learning Sequences and Time Series David M. Burns, Cari M. Whyne 2018 all top 5 Cited by 11 Authors 1 Androz, Guillaume 1 Divo, Felix 1 Faouzi, Johann 1 Holtz, Chester 1 Kolar, Kushal 1 Payne, Marie 1 Rußwurm, Marc 1 Tavenard, Romain 1 Vandewiele, Gilles 1 Woods, Eli 1 Yurchak, Roman Cited in 1 Serial 1 Journal of Machine Learning Research (JMLR) Cited in 1 Field 1 Computer science (68-XX) Citations by Year