Description

In this notebook, you'll find an application of LightGBM - a gradient-boosting machine model that has proven itself as one of the best forecasting methods (a.o. in the M5 forecasting competition). 

We go through the following steps:

1. Importing the required packages
2. Importing the data
3. Feature engineering
4. Model training
5. Submission

There's tons of ways to improve this notebook - better feature engineering, better cross-validation, different loss functions, tune the hyperparameters, etc, but wanted to share for those who'd like to experiment with MLForecast methods.

Disclaimer: I work for Nixtla, authors of MLForecast.