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Hey @Tyler Nisonoff, Thanks for using our library. It seems to me that you are looking for the

cross_validation

method. • https://nixtla.github.io/neuralforecast/core.html#neuralforecast.cross_validation • https://nixtla.github.io/statsforecast/examples/crossvalidation.html

Thanks for the response @Kin Gtz. Olivares! Im not sure thats what im looking for though -- I want to use the model over many days in the future, given that Ive trained it at some point in the past. So imagine today i train a model using historical data. and then over the next month, I want to make daily predictions. Is cross_validation still the recommended tool for that?

Save and Load? https://nixtla.github.io/neuralforecast/examples/save_load_models.html

Hi @Tyler Nisonoff. Can you give us more details of the use case? Here are three different cases: • Today you want to make the forecast for the next 30 days. Without waiting for the newer data you will need to recursively produce forecasts (not recommended, and not supported), using one forecast as input for the next day. • Make 1 daily prediction each day during the next month, using the latest data as each day finishes. You can save the model and load it every new day as Kin mentioned. • Simulate historic forecasts for the last 30 days (you already have the real data). In this case you can use

cross_validation

. Does this help? Which use case are you considering?

Thanks Kin / Chistian! The use-case I'm going for is (2). My problem isn't the save / load -- but rather if I train up to, say, may 10th, predicting may 11th works fine, but if I call:

nf.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 helpful

thanks for the additional details. The problem is that the predict function does not "update" the stored dataset, so by default it can only predict the immediate values after the train set. However, you can pass the new data each time using the

df

parameter of the

predict

function and the dates will match!

ahhh okay thank you I'll test that out tomorrow! Maybe I'm using Future exogenous variables incorrectly then. If I'm trying to predict may 12, I was having the forecast variables (say a generation forecast) for may 12 be set in a dataframe with

ds ==  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

yes, all the information must match their actual dates. So if you have a forecast for may 12 at 6pm, use that datestamp. Also note that the

futr_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

.

great that all makes sense, thank you!

Think of the

predict

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.

awesome, thanks again for the help!