Training models with backtesting
Building on the previous exercises, you'll now evaluate your models using backtesting. You'll define 4 partitions, each with a 12-hour shift and a 72-hour testing window, and execute the process with the cross_validation() method.
The ts DataFrame and initialized MLForecast object (mlf) are preloaded, so you can focus on setting up and running the backtesting. Let's get started!
Este exercício faz parte do curso
Designing Forecasting Pipelines for Production
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Import a library for interval calibration
from mlforecast.utils import ____
# Set parameters
h = ____
step_size = ____
partitions = 4
n_windows = 3
method = "conformal_distribution"
levels = [95]
# Initialize PredictionIntervals
pi = ____(h=____, n_windows=____, method=____)