Tyler Nisonoff
05/13/2023, 12:58 PMnf.predict(futr_df=<day-to-pred>)
However, it seems that this always returns a dataframe with a ds column with just the next 24 hours after where I stopped training.
Is there some way to apply to model to the next N days without retraining it every time? or would i have to retrain / finetune on the data since then? Perhaps the latter is the only way to support historical features?Kin Gtz. Olivares
05/13/2023, 1:33 PMcross_validation
method.
• https://nixtla.github.io/neuralforecast/core.html#neuralforecast.cross_validation
• https://nixtla.github.io/statsforecast/examples/crossvalidation.htmlTyler Nisonoff
05/13/2023, 1:40 PMKin Gtz. Olivares
05/13/2023, 1:59 PMCristian (Nixtla)
05/14/2023, 8:36 PMcross_validation
.
Does this help? Which use case are you considering?Tyler Nisonoff
05/14/2023, 10:55 PMnf.predict(futr_df=to_pred)
where to_pred
is a dataframe with a ds
column with ds = May 12th and may 12ths Exogenous Variables, the returning DF will have a ds column for may 11th.
The predictions seem reasonable, so I'm just hacking around this by changing the datatimes returned, but I took it as a signal that maybe I'm doing something wrong.
I can try to come up with a simple repro tomorrow if helpfulCristian (Nixtla)
05/14/2023, 10:59 PMdf
parameter of the predict
function and the dates will match!df
to predict the future values after df
(and use futr_df
for the future exogenous variables)Tyler Nisonoff
05/14/2023, 11:22 PMds == may 11
and trying to pass that into futur_df, but I thinkk what you're saying is it should be set for ds == may 12
, and I should pass that as futr_df
and the rest of the historical data in as df
Cristian (Nixtla)
05/14/2023, 11:24 PMfutr_df
has to have the exact same variables used for training, and can only have 1 forecast horizon (24 hs for example). And lastly, should have the immediate values after the database in df
.df
as well! if not, it will use the stored information from the training data.Tyler Nisonoff
05/14/2023, 11:26 PMdf
and futr_df
be (in terms of rows)? seems like futr_df
would have 24 rows, and if im predicting may 12, they'd have ds for all the may 12 hours. If ive trained up to, may 10th, I'm a bit confused what the df
should look likeCristian (Nixtla)
05/15/2023, 12:00 AMpredict
function as the forward
step of the model. It will receive `df`+`futr_df` as inputs.
• df
should end at 11pm may 11 (or the last timestamp before the one you want to predict), and have at least 5*24 rows if you only have 1 time series (the function is intelligent, if you pass more data, it will only use the last 5*24 timestamps).
• futr_df
should have the exogenous future values for the date you are forecasting, may 12 in this case, with 24 rows.
This applies for any date you are forecasting, once you pass a new df
, it will not use the training data ending in may 10th.Tyler Nisonoff
05/15/2023, 12:02 AM