An end-to-end tutorial on using a global forecasting model (i.e., LightGBM) on a retail sales dataset (the M5 competition) using multi-step recursive forecasting.

This tutorial emulates a batch forecasting workflow, breaking the process into multiple steps:

0. Obtain the raw data.
1. Create the base dataset containing sales, price, promos etc. by product id and date.
2. Create a feature engineering pipeline, create the training data, and store the pipeline and training data.
3. Train the model and store it.
4. Create a forecast using recursive forecasting.
5. Plot the forecast, feature importance, and other model diagnostics.

For more on feature engineering for time series forecasting check out this course.

This tutorial requires:

• numpy
• pandas
• scikit-learn
• joblib
• matplotlib
• sktime
• jupyter
• pyarrow
• lightgbm

These can be installed by from the requirements.txt.

pip install -r requirements.txt

The notebooks were run on Python 3.10.2.