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Hey <@U045VS8T481>! Thanks for letting us know about the issue.

The new version of StatsForecast includes a new parameter named `fallback_model.`

The main idea behind it is that you can pass a safe model in case that a main model fails. So, StatsForecast tries to fit the main models for each time series and if something fails, it will fit a safer model, for example `Naive().`

The code would be,

```StatsForecast(models=AutoARIMA(), fallback_model=Naive(), extra params) 
```
Please let us know if that works for your use case.

<@U0316L4HWQ7> thanks Fede… will try. now the next question is how i would know which forecast using fallback vs original?

also, if i change Naive() to ETS, it erros out: TypeError: forecast() missing 1 required positional argument: ‘self’