Forecasting with ML Models
As a data science consultant, your task is to predict US hourly electricity demand. In the previous task, you cleaned and prepared the data. Now, it's time to use machine learning models to build your forecast.
We previously covered the statsforecast workflow, and now you'll apply the same principles using mlforecast.
The train and test datasets, as well as models (LGBMRegressor(), XGBRegressor(), LinearRegression()), are preloaded.
The MLForecast class has been imported from the mlforecast package, ready to use. Let's build your forecast!
Este exercício faz parte do curso
Designing Forecasting Pipelines for Production
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Define the ML models
ml_models = [____(), XGBRegressor(), LinearRegression()]
# Set up the MLForecast object with models and frequency
mlf = ____(
models= ____,
freq='____',
lags=list(range(1, 24)),
date_features=['year', 'month', 'day', 'dayofweek', 'quarter', 'week', 'hour'])