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!
Den här övningen är en del av kursen
Designing Forecasting Pipelines for Production
Interaktiv övning med praktiskt arbete
Testa den här övningen genom att slutföra den här exempelkoden.
# 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=____)