Creating the Prophet cross-validation DataFrame
To perform cross-validation in Prophet, first you need a fitted model. So, we'll begin with the same procedure we've completed throughout this book. This dataset is very cooperative so we'll be able to use plenty of Prophet's default parameters. We will plot the changepoints, so be sure to include that function with your other imports before loading the data:
import pandas as pd
import matplotlib.pyplot as plt
from fbprophet import Prophet
from fbprophet.plot import add_changepoints_to_plot
df = pd.read_csv('online_retail.csv')
df['date'] = pd.to_datetime(df['date'])
df.columns = ['ds', 'y']
This dataset does not have very complicated seasonality, so we'll reduce the Fourier order of yearly seasonality when instantiating our model, but keep everything else default, before fitting, predicting, and plotting. We'll use a 1-year future forecast:
model...
