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.

Este exercício faz parte do curso

Designing Forecasting Pipelines for Production

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Instruções do exercício

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").

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# 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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