https://github.com/probabl-ai/forecasting

https://github.com/probabl-ai/forecasting

Last synced: 5 months ago
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• Host: GitHub
• URL: https://github.com/probabl-ai/forecasting
• Owner: probabl-ai
• License: mit
• Created: 2025-06-23T21:29:15.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-07-10T12:10:19.000Z (6 months ago)
• Last Synced: 2025-07-10T17:55:05.656Z (6 months ago)
• Language: Python
• Homepage: https://probabl-ai.github.io/forecasting/
• Size: 103 MB
• Stars: 1
• Watchers: 0
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Tutorial on Forecasting with Machine Learning

## Browse the contents online

- [![Launch JupyterLite](./book/images/jupyterbook_badge.svg 'Our JupyterBook

website')](https://probabl-ai.github.io/forecasting/)

Static HTML with precomputed results.

- [![Launch JupyterLite](./book/images/jupyterlite_badge.svg 'Our JupyterLite

website')](https://probabl-ai.github.io/forecasting/jupyterlite/lab)

In-browser interactive execution environment, no install needed.

## Local setup

### Install `pixi`

You can refer to the [official website](https://pixi.sh/latest/#installation) for

installation.

### Launching Jupyter Lab

To launch Jupyter Lab, run the following command:

```bash

pixi run jupyter lab

```

The Python environment and necessary packages will be automatically installed for you.

### Opening lecture notes

The lecture notes are available in the `content/python_files` directory. To open the

Python file as notebook, you need to right click on the file and select `Open with` ->

`Notebook`.

Alternatively, you can generate notebooks as well:

```bash

pixi run -e doc convert-to-notebooks

```

This will convert the Python files into notebooks in the folder `content/notebooks`.

### Building and testing the jupyterlite deployment locally

Test the deployment locally:

```bash

pixi run -e doc serve-jupyterlite

```

Then open http://[::]:8000/ in a web browser.

Note: firefox often complains about Service Workers not being initialized when

running jupyterlite served from localhost. This prevents accessing the local

dataset files. Use a different browser to test the local deployment.