Defining the forecasting pipeline

Now you'll define the forecasting model and parameters for the MLForecast pipeline. This step prepares the model configuration that will be used for time series forecasting in the pipeline.

Deze oefening maakt deel uit van de cursus

Designing Forecasting Pipelines for Production

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Oefeninstructies

Import LGBMRegressor from lightgbm.
Instantiate a LGBMRegressor model with 100 estimators and a learning rate of 0.05.
Create a dictionary named params that includes the frequency ("h"), lags (1-24), and date features ("month", "day", "dayofweek", "week", and "hour").

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import LGBMRegressor from lightgbm
from ____ import ____

# Instantiate the model
model = ____(n_estimators=____, learning_rate=____)

# Set the model parameters
params = {
  "freq": "____",
  "lags": list(range(____, ____)),
  "date_features": ["month", "day", "____", "____", "hour"]
}

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