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!
Diese Übung ist Teil des Kurses
Designing Forecasting Pipelines for Production
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
# 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=____)