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!
Questo esercizio fa parte del corso
Designing Forecasting Pipelines for Production
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# 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=____)