if yes, can someone tell me more about the logic of

insample_size

? it looks like I get widely different return values than the number I pass in. I’d expect insample_size=100 to return 100 in_sample predictions, which looks like is the case, but 110 returns 1100, which seems to be really weird.

I want to integrate neuralforecast into our TS cross validation library, this is the way I’m trying to produce in_sample forecasts: https://github.com/dream-faster/fold-models/pull/9

but I think I’m accidentally using out-of-sample cross-validation and not getting the insample predictions

The basic usage of the

predict_rolled

function is to forecast multiple windows. You can use this function to forecast any fixed number of

n_windows

or subset of your dataset

Y_df

. The

insample_size

in this case is equivalent to the

test_size

of the

cross_validation

method. Anything before the

insample_size

is discarded (except for the last

input_size

window used as inputs for the first forecast).

For example, if you have your dataset of length 1,000, and you set

insample_size

to 900, you will have all the forecasts starting at the 100th timestamp. Also consider adjusting the

step_size

, the default is 1, so it will produce forecasts of size

h

at every timestamp.

this is what we ended up using, does this look correct to you? thank you!

@Asterios Tsiourvas here