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.

本练习是课程的一部分

Designing Forecasting Pipelines for Production

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练习说明

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.

交互式实操练习

通过完成这段示例代码来试试这个练习。

# 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(____)

编辑并运行代码