import pmdarima as pm
from pmdarima import model_selection
print("pmdarima version: %s" % pm.__version__)
# Load the data and split it into separate pieces
data = pm.datasets.load_wineind()
train, test = model_selection.train_test_split(data, train_size=165)
# Even though we have a dedicated train/test split, we can (and should) still
# use cross-validation on our training set to get a good estimate of the model
# performance. We can choose which model is better based on how it performs
# over various folds.
model1 = pm.ARIMA(order=(2, 1, 1), seasonal_order=(0, 0, 0, 1))
model2 = pm.ARIMA(order=(1, 1, 2), seasonal_order=(0, 1, 1, 12))
cv = model_selection.SlidingWindowForecastCV(window_size=100, step=24, h=1)
model1_cv_scores = model_selection.cross_val_score(
model1, train, scoring='smape', cv=cv, verbose=2)
model2_cv_scores = model_selection.cross_val_score(
model2, train, scoring='smape', cv=cv, verbose=2)
print("Model 1 CV scores: {}".format(model1_cv_scores.tolist()))
print("Model 2 CV scores: {}".format(model2_cv_scores.tolist()))
# Pick based on which has a lower mean error rate
m1_average_error = np.average(model1_cv_scores)
m2_average_error = np.average(model2_cv_scores)
errors = [m1_average_error, m2_average_error]
models = [model1, model2]
# 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()