Installation

Using conda

The easiest way to install tslearn is probably via conda:

conda install -c conda-forge tslearn

Using PyPI

Using pip should also work fine:

python -m pip install tslearn

In this case, you should have numpy, cython and C++ build tools available at build time.

Using latest github-hosted version

If you want to get tslearn’s latest version, you can refer to the repository hosted at github:

python -m pip install https://github.com/tslearn-team/tslearn/archive/master.zip

In this case, you should have numpy, cython and C++ build tools available at build time.

It seems on some platforms Cython dependency does not install properly. If you experiment such an issue, try installing it with the following command:

python -m pip install cython

before you start installing tslearn. If it still does not work, we suggest you switch to conda installation.

Other requirements

tslearn builds on (and hence depends on) scikit-learn, numpy and scipy libraries.

If you plan to use the tslearn.shapelets module from tslearn, tensorflow (v2) should also be installed. h5py is required for reading or writing models using the hdf5 file format. In order to load multivariate datasets from the UCR/UEA archive using the tslearn.datasets.UCR_UEA_datasets class, installed scipy version should be greater than 1.3.0.