tslearn
is a Python package that provides machine learning tools for the analysis of time series.
This package builds on scikit-learn
, numpy
and scipy
libraries.
If you would like to contribute to tslearn
, please have a look at our contribution guidelines.
Cython
numpy
scipy
scikit-learn
If you plan to use the shapelets
module, keras
should also be installed.
The easiest way to install tslearn
is probably via pip
:
pip install tslearn
If you want to get tslearn
's latest version, you can refer to the repository hosted at github:
pip install git+https://github.com/rtavenar/tslearn.git
It seems on some platforms Cython
dependency does not install properly.
If you experiment such an issue, try installing it with the following command:
pip install cython
before you start installing tslearn
.
The documentation, including a gallery of examples, is hosted at readthedocs.
- A
generators
module provides Random Walks generators - A
datasets
module provides access to the famous UCR/UEA datasets through theUCR_UEA_datasets
class - A
preprocessing
module provides standard time series scalers - A
metrics
module provides:- Dynamic Time Warping (DTW) (with Sakoe-Chiba band and Itakura parallelogram variants)
- LB_Keogh
- Global Alignment Kernel
- Soft-DTW from Cuturi and Blondel
- A
neighbors
module includes nearest neighbor algorithms to be used with time series - An
svm
module includes Support Vector Machine algorithms with:- Standard kernels offered in
sklearn
(with adequate array reshaping done for you) - Global Alignment Kernel
- Standard kernels offered in
- A
clustering
module includes the following time series clustering algorithms:- Standard Euclidean k-means (with adequate array reshaping done for you)
- Based on
tslearn.barycenters
- Based on
- DBA k-means from Petitjean et al.
- Based on
tslearn.barycenters
that offers DBA facility that could be used for other applications than just k-means
- Based on
- Global Alignment kernel k-means
- KShape clustering from Paparizzos and Gravano
- Soft-DTW k-means from Cuturi and Blondel
- Based on
tslearn.barycenters
that offers Soft-DTW barycenter computation
- Based on
- It also provides a way to compute the silhouette coefficient for given clustering and metric
- Standard Euclidean k-means (with adequate array reshaping done for you)
- A
shapelets
module includes an efficient implementation of the Learning Time-Series method from Grabocka et al.- Warning: to use the
shapelets
module, an extra dependency is required:keras
- Warning: to use the
- A
piecewise
module includes standard time series transformations, as well as the corresponding distances:- Piecewise Aggregate Approximation (PAA)
- Symbolic Aggregate approXimation (SAX)
- 1d-Symbolic Aggregate approXimation (1d-SAX)
Have a look there for a list of suggested features. If you want other ML methods for time series to be added to this TODO list, do not hesitate to open an issue! See our contribution guidelines for more information about how to proceed.
Authors would like to thank Mathieu Blondel for providing code for
Kernel k-means and Soft-DTW (both
distributed under BSD license) that is used in the clustering
module of this library.
If you use tslearn
in a scientific publication, we would appreciate citations:
@misc{tslearn,
title={tslearn: A machine learning toolkit dedicated to time-series data},
author={Tavenard, Romain},
year={2017},
note={\url{https://github.com/rtavenar/tslearn}}
}