Comments (5)
@pst2154, thanks for the issue. I think your message got lost during formating or something.
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fixed
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Hi @pst2154. There are a couple of ways you can achieve this:
- Using MLForecast.cross_validation. This allows you to evaluate the performance of your model across several validation sets. You can train a single model by setting
refit=False
, which should speed things up. - Using LightGBMCV. This does something similar but trains several models simultaneously, which allows you to perform early stopping based on the performance across the whole forecasting window. There's an example on how to do this along with trial pruning in the m4-cv notebook.
We suggest using any of these approaches as opposed to just regular methods because otherwise you may find a model that is very good at predicting the next step but doesn't perform as well over your complete forecast horizon.
Please let us know if you have further doubts.
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Good point, will try that, thanks!
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We also have this tutorial on cross validation.
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