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.

Cet exercice fait partie du cours

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

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Instructions de l’exercice

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").

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# 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"]
}

Modifier et exécuter le code