Summarize

.gitignore

@@ -90,3 +90,8 @@ jupyterlite/.jupyterlite.doit.db
 
 # requests cache for weather data download
 .cache.sqlite
+
+# Slides files
+content/slides/_freeze/
+content/slides/_site/
+

README.md

@@ -1,13 +1,13 @@
-# Tutorial on Forecasting with Machine Learning
+# Tutorial on Skrub DataOps applied to forecasting timeseries
 
 ## Browse the contents online
 
 - [![Launch JupyterLite](./book/images/jupyterbook_badge.svg 'Our JupyterBook
-website')](https://probabl-ai.github.io/forecasting/)
+website')](skrub-data.org/EuroSciPy2025/)
 Static HTML with precomputed results.
 
 - [![Launch JupyterLite](./book/images/jupyterlite_badge.svg 'Our JupyterLite
-website')](https://probabl-ai.github.io/forecasting/jupyterlite/lab)
+website')](skrub-data.org/EuroSciPy2025/jupyterlite/lab)
 In-browser interactive execution environment, no install needed.
 
 ## Local setup

book/_config.yml

@@ -1,8 +1,8 @@
 # Book settings
 # Learn more at https://jupyterbook.org/customize/config.html
 
-title: Forecasting with machine learning
-author: Guillaume Lemaitre & Olivier Grisel
+title: Skrub DataOps applied to forecasting timeseries
+author: Guillaume Lemaitre & Olivier Grisel & Riccardo Cappuzzo
 logo: logo.svg
 
 # Force re-execution of notebooks on each build.
@@ -22,7 +22,7 @@ bibtex_bibfiles:
 
 # Information about where the book exists on the web
 repository:
-  url: https://github.com/probabl-ai/forecasting
+  url: https://github.com/skrub-data/EuroSciPy2025
   path_to_book: book
   branch: main
 

book/_toc.yml

@@ -5,11 +5,8 @@ format: jb-book
 root: intro
 parts:
 
-- caption: Forecasting with Machine Learning
+- caption: Skrub DataOps applied to forecasting timeseries
   chapters:
   - file: content/notebooks/fetch_weather_data
   - file: content/notebooks/feature_engineering
   - file: content/notebooks/single_horizon_prediction
-  - file: content/notebooks/multiple_horizons_prediction
-  - file: content/notebooks/prediction_intervals
-  - file: content/notebooks/direct_vs_recursive_forecasting

book/intro.md

@@ -1,12 +1,14 @@
-# Forecasting with machine learning
+# Skrub DataOps applied to forecasting timeseries
 
-This tutorial introduces how to use machine learning to forecast time series data.
+This tutorial introduces the `skrub`
+[DataOps](https://skrub-data.org/stable/userguide_data_ops.html) to a forecasting
+timeseries use case.
 
 ## Table of contents
 
 ### Slides
 
-### Notebooks [![Launch JupyterLite](/images/jupyterlite_badge.svg 'Our JupyterLite website')](https://probabl-ai.github.io/forecasting/jupyterlite/lab)
+### Notebooks [![Launch JupyterLite](/images/jupyterlite_badge.svg 'Our JupyterLite website')](https://skrub-data.org/EuroSciPy2025/jupyterlite/lab)
 
 ```{tableofcontents}
 ```