Fitting the model

Now that your model and parameters are ready, you'll initialize MLForecast and fit it to the time series data.

The model and params variables from the previous exercise are available, along with the ts DataFrame.

Deze oefening maakt deel uit van de cursus

Designing Forecasting Pipelines for Production

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Oefeninstructies

Create an MLForecast instance named mlf.
Set the freq, lags, and date_features arguments using the respective keys from the params dictionary.
Fit the model to the ts DataFrame.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create an MLForecast instance
mlf = ____(
    # Set the freq, lags, and date_features arguments
  	models=model,
    freq=params["____"],
    lags=params["____"],
    date_features=params["____"]
)

# Fit mlf to the time series data
mlf.fit(____)

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