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.

この演習はコースの一部です

Designing Forecasting Pipelines for Production

コースを見る

演習の手順

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.

実践的なインタラクティブ演習

このサンプルコードを完成させて、この演習に挑戦してみましょう。

# 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(____)

コードを編集して実行