One more question : ), I am trying to follow the MSTL tutorial: https://nixtla.github.io/statsforecast/docs/tutorials/multipleseasonalities.html This section throws an exception for me and I am unable to get around it:

models = [MSTL(
    season_length=[24, 24 * 7], # seasonalities of the time series 
    trend_forecaster=AutoARIMA() # model used to forecast trend
)]

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

I get the following exception when I include the level parameter in the predict method:

Exception: You have to instantiate either the trend forecaster class or MSTL class with `prediction_intervals` to calculate them

(Without the level=[90] parameter everything seem to work fine) I have also tried adding the conformal intervals:

MSTL(
        season_length=[24, 24*7],
        trend_forecaster=AutoARIMA(),
        prediction_intervals=ConformalIntervals(
            h = 3, # fcst horizon
            n_windows = 10 # number of CV windows, (conformal scores)
            )
    )

This also causes the predict method to crash with the following error:

ValueError: operands could not be broadcast together with shapes (1,24) (10,3)

I have statsforecasts 1.5.0 btw. Thanks in advance for patience.

So with the Conformal Interval,

h

needs to equal the

predict

. So try

h=24

, and then they can be broadcasted

Thanks so much for such a quick reply

no it goes to the Trend forecaster