def test_basic_arima():
arima = ARIMA(order=(0, 0, 0), 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_arma():
arma = ARIMA(order=(0, 0, 0), suppress_warnings=True)
preds = arma.fit_predict(y) # fit/predict for coverage
# No OOB, so assert none
assert arma.oob_preds_ is None
# test some of the attrs
assert_almost_equal(arma.aic(), 11.201, decimal=3) # equivalent in R
# intercept is param 0
intercept = arma.params()[0]
assert_almost_equal(intercept, 0.441, decimal=3) # equivalent in R
assert_almost_equal(arma.aicc(), 11.74676, decimal=5)
assert_almost_equal(arma.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 = arma.predict(n_periods=10, return_conf_int=True,
alpha=0.05)
assert_array_almost_equal(intervals, expected_intervals)
def run_ARIMA(data, param):
order = param['order']
testsize = param['testsize']
T = data.shape[-1]
T_test = int((T * testsize) // 1)
result_full = np.zeros([data.shape[0], T_test])
total_time = 0
n_round = 0
for i in range(T_test):
y = data[..., i:T - T_test + i].copy()
n_round += 1
start = time.time()
for j in range(y.shape[0]):
model = ARIMA(order,
suppress_warnings=True,
enforce_stationarity=True)
result = model.fit_predict(y[j], n_periods=1)
result_full[j, i] = result[..., -1]
end = time.time()
total_time = total_time + (end - start)
true_value = data[..., -T_test:]
stat = {}
stat['acc'] = get_acc(result_full, true_value)
stat['nrmse'] = nrmse(result_full, true_value)
stat['ave_time'] = total_time / n_round
return (stat)
