# Seglearn: A Python Package for Learning Sequences and Time Series

arXiv: https://arxiv.org/abs/1803.08118 · HTML: https://mlsanity.com/paper/1803.08118/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [Clairvoyance: A Pipeline Toolkit for Medical Time Series](https://mlsanity.com/paper/2310.18688/) (2023)
- [tsflex: flexible time series processing & feature extraction](https://mlsanity.com/paper/2111.12429/) (2021)
- [HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python](https://mlsanity.com/paper/2207.03517/) (2022)
- [TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine](https://mlsanity.com/paper/2301.12260/) (2023)
- [A Review of Open Source Software Tools for Time Series Analysis](https://mlsanity.com/paper/2203.05195/) (2022)

## 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.