Welcome to seglearn documentation!¶
This project is an sklearn extension for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extraction, feature processing, and a final estimator compatible with sklearn model evaluation and parameter optimization tools. Seglearn provides a flexible approach to multivariate time series and contextual data for classification, regression, and forecasting problems. Support and examples are provided for learning time series with classical machine learning and deep learning models.
See the source code repository on github: https://github.com/dmbee/seglearn
The main documentation. This contains an in-depth description of all algorithms and how to apply them.
The exact API of all functions and classes, as given in the docstring. The API documents expected types and allowed features for all functions, and all parameters available for the algorithms.
A set of examples illustrating the use of the different algorithms. It complements the User Guide.