def test_basic_arima():
arima = ARIMA(order=(0, 0, 0), trend='c', suppress_warnings=True)
preds = arima.fit_predict(y) # fit/predict for coverage
# test some of the attrs
assert_almost_equal(arima.aic(), 11.201308403566909, decimal=5)
assert_almost_equal(arima.aicc(), 11.74676, decimal=5)
assert_almost_equal(arima.bic(), 13.639060053303311, decimal=5)
# get predictions
expected_preds = np.array([
0.44079876, 0.44079876, 0.44079876, 0.44079876, 0.44079876, 0.44079876,
0.44079876, 0.44079876, 0.44079876, 0.44079876
])
# generate predictions
assert_array_almost_equal(preds, expected_preds)
# Make sure we can get confidence intervals
expected_intervals = np.array([[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139]])
_, intervals = arima.predict(n_periods=10,
return_conf_int=True,
alpha=0.05)
assert_array_almost_equal(intervals, expected_intervals)
def test_basic_arima():
arima = ARIMA(order=(0, 0, 0), trend='c', suppress_warnings=True)
preds = arima.fit_predict(y) # fit/predict for coverage
# test some of the attrs
assert_almost_equal(arima.aic(), 11.201308403566909, decimal=5)
assert_almost_equal(arima.bic(), 13.639060053303311, decimal=5)
# get predictions
expected_preds = np.array([
0.44079876, 0.44079876, 0.44079876, 0.44079876, 0.44079876, 0.44079876,
0.44079876, 0.44079876, 0.44079876, 0.44079876
])
# generate predictions
assert_array_almost_equal(preds, expected_preds)
