Nixtla's Open Source Time Series Ecosystem.

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I looked it up, and the Super Smoother function is not available in Nixtla, but you can try it with other models. The closest might be <https://nixtla.github.io/statsforecast/src/core/models.html#simplesmooth|SimpleSmooth>, so you can do:
```models = [MSTL(
    season_length=[24, 24 * 7], # seasonalities of the time series 
    trend_forecaster=SimpleExponentialSmoothing() # model used to forecast trend
)]

forecasts = sf.predict(h=24, level=[90])
forecasts.head()```

You also might be able to create your own `StatsForecast` compatible model with an existing <https://github.com/jakevdp/supersmoother|supersmoother> implementation. For example:
```from statsforecast.models import _TS
from supersmoother import SuperSmoother

class _SuperSmoother(_TS):
    def __init__():
        self.model = SuperSmoother()
   
    def fit(self, t, y, dy):
        self.model.fit(t, y, dy)

    def predict(self, tfit):
        self.model.predict(tfit)```
The <https://github.com/Nixtla/statsforecast/blob/main/statsforecast/models.py#L2800-L2969|Naive> model might provide guidance. I’m not sure this will work. You need to shape the output a bit, like `predict` outputs a dictionary, which `StatsForecast` turns into a DataFrame later.

The easiest thing to do is probably approximate it with the SimpleExponentialSmoothing, which is also meant for timeseries without seasonability