def test_predict_nans():
"""
Tests that the predictions are not NaNs
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=Laplace())
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_predict_length():
"""
Tests that the length of the predict dataframe is equal to no of steps h
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2)
x = model.fit()
x.summary()
assert (model.predict(h=5, oos_data=data_oos).shape[0] == 5)
def test_2_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 = ARIMAX(formula="y ~ x1 + x2", data=data, ar=2, ma=2)
x = model.fit()
predictions = model.predict(h=10, oos_data=data_oos, intervals=False)
assert (not np.all(predictions.values == predictions.values[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 = ARIMAX(formula="y ~ x1", data=data, ar=1, ma=1, family=Poisson())
x = model.fit('BBVI', iterations=200, quiet_progress=True)
predictions = model.predict(h=10, oos_data=data_oos, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def test_2_predict_intervals_mh():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMAX(formula="y ~ x1 + x2", data=data, ar=2, ma=2, family=Laplace())
x = model.fit('M-H', nsims=200, quiet_progress=True)
predictions = model.predict(h=10, oos_data=data_oos, intervals=True)
assert(np.all(predictions['99% Prediction Interval'].values > predictions['95% Prediction Interval'].values))
assert(np.all(predictions['95% Prediction Interval'].values > predictions[model.data_name].values))
assert(np.all(predictions[model.data_name].values > predictions['5% Prediction Interval'].values))
assert(np.all(predictions['5% Prediction Interval'].values > predictions['1% Prediction Interval'].values))
def test_predict_intervals_bbvi():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=t())
x = model.fit('BBVI', iterations=100, quiet_progress=True)
predictions = model.predict(h=10, oos_data=data_oos, intervals=True)
assert(np.all(predictions['99% Prediction Interval'].values > predictions['95% Prediction Interval'].values))
assert(np.all(predictions['95% Prediction Interval'].values > predictions[model.data_name].values))
assert(np.all(predictions[model.data_name].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 = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=Poisson())
x = model.fit()
predictions = model.predict(h=10, oos_data=data_oos, 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 a_test2_predict_intervals_mh():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMAX(formula="y ~ x1 + x2",
data=data,
ar=2,
ma=2,
family=Exponential())
x = model.fit('M-H', nsims=400)
predictions = model.predict(h=10, oos_data=data_oos, 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))