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.

To ćwiczenie jest częścią kursu

Designing Forecasting Pipelines for Production

Zobacz kurs

Instrukcje do ćwiczenia

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").

Interaktywne ćwiczenie praktyczne

Spróbuj tego ćwiczenia, uzupełniając ten przykładowy kod.

# 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"]
}

Edytuj i uruchom kod