aeon currently supports Python versions 3.8, 3.9, 3.10 and 3.11. Prior to these instructions, please ensure you have a compatible version of Python installed (i.e. from

aeon is available for most well-known operating systems, and is frequently tested on macOS, Ubuntu and Windows servers by our development CI.

When it comes to installing aeon, there are currently three primary options:

Optional Dependencies#

All installation options include the core dependencies required to run the framework components of aeon. Some estimators and functionality require optional dependencies. Without these dependencies, you may find that you will be prompted to install an additional package when trying to use certain functionality.

For each installation option, we provide a method to install only the core dependencies and a method to install all dependencies (barring certain unstable ones). Installing all dependencies can take a while to process the installation and introduce limitations on the versioning of other packages, but will allow all aeon functionality to be used without impediment.

Install the latest release from PyPi#

We recommend creating a virtual environment for your aeon installation. This will ensure that the dependencies of aeon do not conflict with other packages you may have installed.

aeon releases are available via PyPI. To install the latest aeon release with core dependencies via pip type:

pip install -U aeon

To install aeon with all stable dependencies, install with the all_extras modifier. This will also install core dependencies, so the above command is not required.

pip install -U aeon[all_extras]


Some of the dependencies included in all_extras do not work on Mac ARM-based processors, such as M1, M2, M1Pro, M1Max or M1Ultra. This may cause an error during installation. Mode details can be found in the troubleshooting section below.

After installation, you can verify that aeon has been installed correctly by running the following commands:

pip show aeon  # see information about the installation i.e. version and file location
pip freeze  # see all installed packages for the current environment

For more information on the dependencies of aeon and more dependencies groups (such as only dependencies for deep learning, or a list less stable dependencies excluded from all_extras), see the pyproject.toml configuration file.

Install the latest release from conda-forge#

aeon releases are also available via conda-forge. Run the following to create a new environment for aeon and install the package:

conda create -n aeon-env -c conda-forge aeon
conda activate aeon-env

Post-installation you can verify that aeon has been installed correctly by running the following:

conda list aeon  # see information about the installation i.e. version and file location
conda list  # see all installed packages for the current environment

Currently for conda installations, optional dependencies must be installed separately.

Install the latest development version using pip#

Like the above method, we recommend creating a virtual environment for your aeon installation.

If you already have the latest aeon release or the aeon GitHub main branch installed, you will have to uninstall it prior to following these instructions:

pip uninstall aeon

The latest developments and bugfixes for aeon are available on the aeon GitHub main branch. The main branch can be installed directly from GitHub using pip:

pip install -U git+

To install aeon from GitHub main branch with all stable dependencies, the following command can be used.

pip install -U "aeon[all_extras] @ git+"

The same warnings and information regarding the MacOS ARM processor, checking install versioning and pyproject.toml dependencies given in the previous section apply here as well.

Using a pip venv#

In order to avoid potential conflicts with other packages, we strongly recommended using a virtual environment (venv) or a fresh conda environment for the above installation options.

You can create a virtual environment using the following commands. The name virtual environment name aeon-venv can be replaced with a name of your choosing.

Windows and macOS:

python -m venv aeon-venv


python3 -m venv aeon-venv

This environment can then be activated using the following commands:



macOS and Linux:

source aeon-venv/bin/activate

Note that this will only activate the environment for the current terminal session. If you wish to use the environment in a different terminal session, you will need to activate it again.


If the common errors below do not help, it may be worth checking out the scikit-learn troubleshooting section


The most frequent reason for ModuleNotFoundError is installing aeon with minimum dependencies (i.e. just pip install aeon) and using an estimator which interfaces a package that has not been installed in the environment. To resolve this, install the missing package, or install aeon with maximum dependencies (see above) or install the individual packages as prompted by the error.


Import errors are often caused by an improperly linked virtual environment. Make sure that your environment is activated and linked to whatever IDE you are using. You can find the instructions for doing so in VScode here. If you are using Jupyter Notebooks, follow these instructions for adding your virtual environment as a new kernel for your notebook.

Installing all_extras on Mac with an ARM processor#

If you are using a Mac with an ARM processor, you may encounter an error when installing aeon[all_extras]. This is due to the fact that some libraries included in all_extras are not compatible with ARM-based processors.

The workaround is not to install some of the packages in all_extras and install ARM compatible replacements for others:

  • Do not install the following packages:

    • esig

    • prophet

    • tsfresh

    • tslearn

  • Replace tensorflow package with the following packages:

    • tensorflow-macos

    • tensorflow-metal (optional)

Also, ARM-based processors have issues when installing packages distributed as source distributions instead of Python wheels. To avoid this issue when installing a package, you can try installing it through conda or use a prior version of the package that was distributed as a wheel.