sktime is available via PyPI using:

pip install sktime

But note that the package is actively being developed and currently not feature stable.

Development version

To install the latest development version of sktime, follow these steps:

  1. Download the repository: git clone

  2. Move into the root directory of the repository: cd sktime

  3. Make sure you are on the main branch: git checkout main

  4. Make sure your local version is up-to-date: git pull

  5. Build package from source using: pip install --editable . Please read below for more detailed instructions for specific operating systems.

Building sktime from source also requires

  • Cython >= 0.28.5 (available through pip install cython)

  • OpenMP (see below for instructions)


It is possible to build sktime without OpenMP support by setting the SKTIME_NO_OPENMP environment variable (before cythonization). This is not recommended since it will force some estimators to run in sequential mode and their n_jobs parameter will be ignored.

To run our tests and generate our documentation, you need to follow a few extra steps as described in our section on How to Contribute.

Retrieving other stable versions

If you want to build a previous version, you can run:

``git checkout <VERSION>``

This will checkout the code for that particular version. To see which versions are available, run git tag.

You can also download a zip archive of the version from GitHub.

Building from source

Once you have all the build requirements installed (see below for details), you can build and install the package in the following way.

If you run the development version, it is cumbersome to reinstall the package each time you update the sources. Therefore it’s recommended that you install in editable mode, which allows you to edit the code in-place. This builds the extension in place and creates a link to the development directory (see the pip docs):

pip install --editable .


This is fundamentally similar to using the command python develop (see the setuptool docs). It is however preferred to use pip.


You will have to re-run:

pip install --editable .

every time the source code of a compiled extension is changed (for instance when switching branches or pulling changes from upstream). Compiled extensions are Cython files (ending in .pyx or .pxd).


The default C compiler, Apple-clang, on Mac OSX does not directly support OpenMP. The first solution to build sktime is to install another C compiler such as gcc or llvm-clang. Another solution is to enable OpenMP support on the default Apple-clang. In the following we present how to configure this second option.

You first need to install the OpenMP library:

brew install libomp

Then you need to set the following environment variables:

export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp"
export CFLAGS="$CFLAGS -I/usr/local/opt/libomp/include"
export CXXFLAGS="$CXXFLAGS -I/usr/local/opt/libomp/include"
export LDFLAGS="$LDFLAGS -L/usr/local/opt/libomp/lib -lomp"
export DYLD_LIBRARY_PATH=/usr/local/opt/libomp/lib

Finally you can build the package using the standard command.


The clang compiler included in FreeBSD 12.0 and 11.2 base systems does not include OpenMP support. You need to install the openmp library from packages (or ports):

sudo pkg install openmp

This will install header files in /usr/local/include and libs in /usr/local/lib. Since these directories are not searched by default, you can set the environment variables to these locations:

export CFLAGS="$CFLAGS -I/usr/local/include"
export CXXFLAGS="$CXXFLAGS -I/usr/local/include"
export LDFLAGS="$LDFLAGS -L/usr/local/lib -lomp"
export DYLD_LIBRARY_PATH=/usr/local/lib

Finally you can build the package using the standard command.

For the upcoming FreeBSD 12.1 and 11.3 versions, OpenMP will be included in the base system and these steps will not be necessary.

Installing build dependencies


Installing from source without conda requires you to have installed the sktime runtime dependencies, Python development headers and a working C/C++ compiler. Under Debian-based operating systems, which include Ubuntu:

sudo apt-get install build-essential python3-dev python3-setuptools \

and then:

pip3 install numpy scipy cython

When precompiled wheels are not avalaible for your architecture, you can install the system versions:

sudo apt-get install cython3 python3-numpy python3-scipy python3-matplotlib

On Red Hat and clones (e.g. CentOS), install the dependencies using:

sudo yum -y install gcc gcc-c++ python-devel numpy scipy


To use a high performance BLAS library (e.g. OpenBlas) see scipy installation instructions.


To build sktime on Windows you need a working C/C++ compiler in addition to numpy, scipy and setuptools.

The building command depends on the architecture of the Python interpreter, 32-bit or 64-bit. You can check the architecture by running the following in cmd or powershell console:

python -c "import struct; print(struct.calcsize('P') * 8)"

The above commands assume that you have the Python installation folder in your PATH environment variable.

You will need Build Tools for Visual Studio 2017.


You DO NOT need to install Visual Studio 2019. You only need the “Build Tools for Visual Studio 2019”, under “All downloads” -> “Tools for Visual Studio 2019”.

For 64-bit Python, configure the build environment with:

"C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvarsall.bat" x64

Please be aware that the path above might be different from user to user. The aim is to point to the “vcvarsall.bat” file.

And build sktime from this environment:

python install

Replace x64 by x86 to build for 32-bit Python.

Some users have experienced issues when installing NumPy, particuarly version 1.19.4. Note that a recent Windows update may affect compilation using Visual Studio (see Windows update issue).

If you run into a problem installing the development version and are using Anaconda, try:

  1. Install Anaconda

  2. Create new environment: conda create -n sktime-dev python=3.8

  3. Activate environment: conda activate sktime-dev

  4. Install NumPy (pinned to 1.19.3) from pip: pip install numpy==1.19.3

  5. Install requirements: pip install -r build_tools/requirements.txt

  6. Follow the instructions above to point to “vcvarsall.bat”

  7. Run pip install --verbose --no-build-isolation --editable .

In step 5, you may optionally install the packages in build_tools/requirements.txt that are available from Anaconda’s default channels or Conda-Forge via Conda. Any remaining packages can be added via pip.

Building binary packages and installers

The .whl package and .exe installers can be built with:

pip install wheel
python bdist_wheel bdist_wininst

The resulting packages are generated in the dist/ folder.

Using an alternative compiler

It is possible to use MinGW (a port of GCC to Windows OS) as an alternative to MSVC for 32-bit Python. Not that extensions built with mingw32 can be redistributed as reusable packages as they depend on GCC runtime libraries typically not installed on end-users environment.

To force the use of a particular compiler, pass the --compiler flag to the build step:

python build --compiler=my_compiler install

where my_compiler should be one of mingw32 or msvc.


The installation instruction are adapted from scikit-learn’s advanced installation instructions.