def test_predict_is_nans():
"""
Tests that the in-sample predictions are not nans
"""
model = ARIMA(data=data, ar=2, ma=2, family=t())
x = model.fit()
assert (len(
model.predict_is(h=5).values[np.isnan(
model.predict_is(h=5).values)]) == 0)
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=t())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def test_predict_is_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=t())
x = model.fit()
predictions = model.predict_is(h=10, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
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 a_test_predict_is_intervals_mh():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMA(data=data, ar=2, ma=2, family=Exponential())
x = model.fit('M-H', nsims=400)
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 a_test_predict_is_intervals_bbvi():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMA(data=data, ar=1, ma=0, family=Exponential())
x = model.fit('BBVI', iterations=200)
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 = ARIMA(data=data, ar=2, ma=2, family=Poisson())
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_is_intervals():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMA(data=data, ar=2, ma=2, family=t())
x = model.fit()
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[
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))