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hey @Rachel Yee! Thanks for using mlforecast. You can define a specific

Transform

class to standardize individual time series before fitting and predicting (this accounts for different scales). But you can also specify a

Transform

class to standardize the series globally if you prefer. Here’s an example (standardizing each individual series): https://nixtla.github.io/mlforecast/docs/end_to_end_walkthrough.html#target-transformations. Regarding the

cross_validation

method, the splits are done before fitting the models. Therefore the transformations are only applied to the training data of the specific window. Please let us know if something else needs to be clarified. :)