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.

This exercise is part of the course

Designing Forecasting Pipelines for Production

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Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import MLForecast
from mlforecast import MLForecast

# 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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