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had this problem too. ended up hacking in a solution:

fitted_models = sf.fit().fitted_

You can then pickle these fitted models. Note there may be more than one model. When you want to load them again:

new_sf = StatsForecast(df=df, models=placeholder_models)  # the models you put in here are irrelevant
sf.fitted_ = fitted_models
sf.predict(horizon)

Hey Sam, thanks for the response, from what I gathered

.fit

is callable of

StatsForecast

class. That assumption is based on the fact that when calling

.forecast

method it autofits the models. First question: Would it be better to call

.fit

after initiating the class with all the params? Second question: would

fit

create ?incapsulated list? with all the fitted models? hence this code:

[model.fitted_ for model in sf.fit()]

1. Indeed forecast() fits the models. Only call .fit() then .predict() imo if you want to split these steps for some other reason. 2. Ye it does create a list of fitted models.

okay, so utilising

fit

and

predict

in the case of manual fitting. Would be great to have a PR about creating a

save

method.

Yeah I chatted with them about it. The reason is that the overhead can be a bit big. If you have 1000 timeseries, the weights for each timeseries would be saved so it’s more common to just call the forecast straight. The PR makes sense though

@Sam Miller I dabbled a bit around with fitted models and it is a bit confusing on what's happening under the hood, when looking at

sf.fit().fitted_

I can see replicas of the a single model that was issues, are they same model but fitted with different params? AutoARIMA example:

[array([AutoARIMA], dtype=object), array([AutoARIMA], dtype=object), array([AutoARIMA], dtype=object), array([AutoARIMA], dtype=object), array([AutoARIMA], dtype=objec
t), array([AutoARIMA], dtype=object), array([AutoARIMA], dtype=object), array([AutoARIMA], dtype=object), array([AutoARIMA], dtype=object), array([AutoARIMA], dtype=ob
ject), array([AutoARIMA], dtype=object), array([AutoARIMA], dtype=object)]

there may be a separate model for each unique_id in your data

🤦‍♂️omg that makes 100% sense hahaha

dw it confused me too when i first saw it

is there a way to associate the model with its unique id? incremental increase according to alphabetical order?

i haven't figured that one out yet, but you can probably hack it

Yes the best model for each fitted model for each unique id in your data. So if you try ETS and AutoARIMA, you will see double the models. The unique_id should resolve when you call predict so you shouldn’t need to handle it yourself?

check out this thread @Piotr Pomorski. I think it answers what you were asking.

@Martin Bel this is for statsforecast, I'm using mlforecast, which does not have .fitted_ attribute, unfortunately