"""
============================
Cross-validation predictions
============================
In addition to computing cross-validation scores, you can use cross-validation
to produce predictions. Unlike traditional cross-validation, where folds are
independent of one another, time-series folds may overlap (particularly in a
sliding window). To account for this, folds that forecast the same time step
average their forecasts using either a "mean" or "median" (tunable).
.. raw:: html

"""
print(__doc__)
# Author: Taylor Smith
import numpy as np
import pmdarima as pm
from pmdarima import model_selection
from matplotlib import pyplot as plt
print("pmdarima version: %s" % pm.__version__)
# Load the data and split it into separate pieces
y = pm.datasets.load_wineind()
est = pm.ARIMA(order=(1, 1, 2),
seasonal_order=(0, 1, 1, 12),
suppress_warnings=True)
cv = model_selection.SlidingWindowForecastCV(window_size=150, step=4, h=4)
predictions = model_selection.cross_val_predict(
est, y, cv=cv, verbose=2, averaging="median")
# plot the predictions over the original series
x_axis = np.arange(y.shape[0])
n_test = predictions.shape[0]
plt.plot(x_axis, y, alpha=0.75, c='b')
plt.plot(x_axis[-n_test:], predictions, alpha=0.75, c='g') # Forecasts
plt.title("Cross-validated wineind forecasts")
plt.show()