def test_predict_is_length():
"""
Tests that the prediction IS dataframe length is equal to the number of steps h
"""
model = ARIMA(data=data, ar=2, ma=2, family=Cauchy())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def test_predict_is_length():
"""
Tests that the length of the predict IS dataframe is equal to no of steps h
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=Cauchy())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def test_predict_nans():
"""
Tests that the predictions are not nans
"""
model = ARIMA(data=data, ar=2, ma=2, family=Cauchy())
x = model.fit()
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def test_ppc():
"""
Tests PPC value
"""
model = ARIMA(data=data, ar=2, ma=2, family=Cauchy())
x = model.fit('BBVI', iterations=100, quiet_progress=True)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def test_predict_nonconstant():
"""
We should not really have predictions that are constant (should be some difference)...
This captures bugs with the predict function not iterating forward
"""
model = ARIMA(data=data, ar=2, ma=2, family=Cauchy())
x = model.fit()
predictions = model.predict(h=10, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def test_sample_model():
"""
Tests sampling function
"""
model = ARIMA(data=data, ar=2, ma=2, family=Cauchy())
x = model.fit('BBVI', iterations=100, quiet_progress=True)
sample = model.sample(nsims=100)
assert (sample.shape[0] == 100)
assert (sample.shape[1] == len(data) - 2)
def test_predict_is_nans():
"""
Tests that the predictions in-sample are not NaNs
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=Cauchy())
x = model.fit()
x.summary()
assert (len(
model.predict_is(h=5).values[np.isnan(
model.predict_is(h=5).values)]) == 0)
def test_couple_terms():
"""
Tests the length of the latent variable vector for an ARIMAX model
with 1 AR and 1 MA term, and tests that the values are not nan
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=1, ma=1, family=Cauchy())
x = model.fit()
assert (len(model.latent_variables.z_list) == 5)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_couple_terms_integ():
"""
Tests an ARIMA model with 1 AR and 1 MA term, integrated once, and that
the latent variable list length is correct, and that the estimated
latent variables are not nan
"""
model = ARIMA(data=data, ar=1, ma=1, integ=1, family=Cauchy())
x = model.fit()
assert (len(model.latent_variables.z_list) == 4)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_no_terms():
"""
Tests an ARIMA model with no AR or MA terms, and that
the latent variable list length is correct, and that the estimated
latent variables are not nan
"""
model = ARIMA(data=data, ar=0, ma=0, family=Cauchy())
x = model.fit()
assert (len(model.latent_variables.z_list) == 2)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_2_predict_length():
"""
Tests that the length of the predict dataframe is equal to no of steps h
"""
model = ARIMAX(formula="y ~ x1 + x2",
data=data,
ar=2,
ma=2,
family=Cauchy())
x = model.fit()
x.summary()
assert (model.predict(h=5, oos_data=data_oos).shape[0] == 5)
def test_predict_is_intervals_mh():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMA(data=data, ar=2, ma=2, family=Cauchy())
x = model.fit('M-H', nsims=200, quiet_progress=True)
predictions = model.predict_is(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values >
predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values >
predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values >
predictions['1% Prediction Interval'].values))
def test_predict_is_intervals_bbvi():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=Cauchy())
x = model.fit('BBVI', iterations=100, quiet_progress=True)
predictions = model.predict_is(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values >
predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values >
predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values >
predictions['1% Prediction Interval'].values))
def test_predict_intervals():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMA(data=data, ar=2, ma=2, family=Cauchy())
x = model.fit()
predictions = model.predict(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values >
predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values >
predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values >
predictions['1% Prediction Interval'].values))
def test_2_predict_nans():
"""
Tests that the predictions are not NaNs
"""
model = ARIMAX(formula="y ~ x1 + x2",
data=data,
ar=2,
ma=2,
family=Cauchy())
x = model.fit()
x.summary()
assert (len(
model.predict(h=5, oos_data=data_oos).values[np.isnan(
model.predict(h=5, oos_data=data_oos).values)]) == 0)
def test_2_no_terms():
"""
Tests the length of the latent variable vector for an ARIMAX model
with no AR or MA terms, and two predictors, and tests that the values
are not nan
"""
model = ARIMAX(formula="y ~ x1 + x2",
data=data,
ar=0,
ma=0,
family=Cauchy())
x = model.fit()
assert (len(model.latent_variables.z_list) == 4)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)