Add workflow example for time series forecasting

[hcho3]

• Use data from the M5 competition, a well-known competition for time series forecasting
• Use Dask Kubernetes to run hyperparameter optimization
• Use GPU-accelerated XGBoost and cuDF to accelerate time series analysis end-to-end
• All preprocessing logic uses cuDF.

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[hcho3]

@jacobtomlinson For the time being, I put in the extra notebooks under the toctree of the start_here.ipynb. I did so because I didn't want to clutter the workflow catalog.

[jacobtomlinson]

Thanks @hcho3 sorry it has taken me so long to get a review on this one.

Overall I think the structure is great, I've added a few comments.

One high-level question I had is why are there so many separate pre-processing notebooks? Some of them seem to load the data, do some small transformation and save them again. I think it would be more pleasant to read if there were fewer (or just one) pre-processing notebook. What do you think?

Also can you add some tags to the start-here notebook?

[jacobtomlinson]

This is looking really great. Thanks for taking the time to merge this into one notebook and
address the review feedback.

The only small nit I have is that the DISABLE_JUPYTER env var is no longer needed, so you can remove that from the code cell.