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.

Cet exercice fait partie du cours

<cours>Designing Forecasting Pipelines for Production</cours>

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Instructions de l’exercice

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.

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# 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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