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xgboostftw t1_j03y82b wrote

Seems like a poorly planned attempt at promoting your own tool.
Looking briefly at the notebook, it seems like a lot of the M5 features were excluded and only item_id was kept: https://nixtla.github.io/statsforecast/examples/aws/statsforecast.html#read-data
M5 has additional features like department, category, store, state and of course the events table. These features are very helpful and would obviously be present in a real life scenario of a retail forecast (among with many others).
The code with parameters to train AWS Forecasts models seems to also be missing from the "reproducible experiment" notebook 😂.
Not sure the study is worth taking seriously. Seems like a quick attempt at marketing rather than a study with any meaningful level of rigor. "My Corolla is faster and cheaper than a Porsche 911 when I use vegetable oil to fuel them and don't show you the Porsche".
Where does your result land on the Kaggle leaderboard?

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fedegarzar OP t1_j048qe0 wrote

Here is the step-by-step guide to reproducing Amazon Forecast: https://nixtla.github.io/statsforecast/examples/aws/amazonforecast.html

As you can see, all the exogenous variables of M5 are included in Amazon Forecast.

Concretely, if you read the same link you posted, we even provide links to the Static and temporal exogenous variables you mention.

From the ReadMe:

The data are ready for download at the following URLs:

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