The callback worked! Thanks a bunch @José Morales It revealed a problem in my calls to

.predict()

Does

X_df

need to be sorted in a certain way? When I run

.predict()

on my dataset (multiple time series), the final features from the callback show that my dynamic features (

X_df

) are misaligned with the unique_id & ds pairs from the lags and static features. I think I see the same pattern in the M4 data, so I must be approaching X_df wrong? Details in thread

Here's code I used

The training data

Here are the last 6 rows of each series, which will feed into the lags and dynamic feature used at prediction

When I inspect the features using the callback, the dynamic feature from X_df (

time_id

) appears to be misaligned We are predicting the next 3 steps for each of the two series For the first row, this is clearly from

H1

. time_id is the last time_id in training data + 1, and lag1 is the last y. But for the second row, it looks like we have the time_id from the next row of

H1

but the lags from

H196

Am I passing

X_df

incorrectly? Thanks in advance for any insights!

Everything works as expected now with 0.10.0! Thanks @José Morales!