What's the best way to split time series data into train/test/validation sets, where the validation set would be used for hyperparameter tuning?
We have 3 years' worth of daily sales data, and our plan is to use 2015-2016 as the training data, then randomly sample 10 weeks from the 2017 data to be used as the validation set, and another 10 weeks from 2017 data for the test set. We'll then do a walk forward on each of the days in the test and validation set.