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_the_r_src():
# this is the test the R code provides
fit = ARIMA(order=(2, 0, 1), trend='c', suppress_warnings=True).fit(abc)
# the R code's AIC = 135.4
assert abs(135.4 - fit.aic()) < 1.0
# the R code's AICc = ~ 137
assert abs(137 - fit.aicc()) < 1.0
# the R code's BIC = ~145
assert abs(145 - fit.bic()) < 1.0
# R's coefficients:
# ar1 ar2 ma1 mean
# -0.6515 -0.2449 0.8012 5.0370
arparams = fit.arparams()
assert_almost_equal(arparams, [-0.6515, -0.2449], decimal=3)
maparams = fit.maparams()
assert_almost_equal(maparams, [0.8012], decimal=3)
# > fit = forecast::auto.arima(abc, max.p=5, max.d=5,
# max.q=5, max.order=100, stepwise=F)
fit = auto_arima(abc, max_p=5, max_d=5, max_q=5, max_order=100,
seasonal=False, trend='c', suppress_warnings=True,
error_action='ignore')
assert abs(135.28 - fit.aic()) < 1.0 # R's is 135.28
def test_the_r_src():
# this is the test the R code provides
fit = ARIMA(order=(2, 0, 1), trend='c', suppress_warnings=True).fit(abc)
# the R code's AIC = ~135
assert abs(135 - fit.aic()) < 1.0
# the R code's AICc = ~ 137
assert abs(137 - fit.aicc()) < 1.0
# the R code's BIC = ~145
assert abs(145 - fit.bic()) < 1.0
# R's coefficients:
# ar1 ar2 ma1 mean
# -0.6515 -0.2449 0.8012 5.0370
# note that statsmodels' mean is on the front, not the end.
params = fit.params()
assert_almost_equal(params, np.array([5.0370, -0.6515, -0.2449, 0.8012]),
decimal=2)
# > fit = forecast::auto.arima(abc, max.p=5, max.d=5,
# max.q=5, max.order=100, stepwise=F)
fit = auto_arima(abc, max_p=5, max_d=5, max_q=5, max_order=100,
seasonal=False, trend='c', suppress_warnings=True,
error_action='ignore')
# this differs from the R fit with a slightly higher AIC...
assert abs(137 - fit.aic()) < 1.0 # R's is 135.28
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 test_with_seasonality1():
fit = ARIMA(order=(1, 1, 1),
seasonal_order=(0, 1, 1, 12),
suppress_warnings=True).fit(y=wineind)
_try_get_attrs(fit)
# R code AIC result is ~3004
assert abs(fit.aic() - 3004) < 100 # show equal within 100 or so
# R code AICc result is ~3005
assert abs(fit.aicc() - 3005) < 100 # show equal within 100 or so
# R code BIC result is ~3017
assert abs(fit.bic() - 3017) < 100 # show equal within 100 or so
# show we can predict in-sample
fit.predict_in_sample()
# test with SARIMAX confidence intervals
fit.predict(n_periods=10, return_conf_int=True, alpha=0.05)