def test_issue_341():
y = [
0, 132, 163, 238, 29, 0, 150, 320, 249, 224, 197, 31, 0, 154, 143, 132,
135, 158, 21, 0, 126, 100, 137, 105, 104, 8, 0, 165, 191, 234, 253,
155, 25, 0, 228, 234, 265, 205, 191, 19, 0, 188, 156, 172, 173, 166,
28, 0, 209, 160, 159, 129, 124, 18, 0, 155
]
with pytest.raises(ValueError) as ve:
auto.auto_arima(y,
start_p=1,
start_q=1,
test='adf',
max_p=3,
max_q=3,
m=52,
start_P=0,
seasonal=True,
d=None,
D=1,
trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
# assert that we catch the np LinAlg error and reraise with a more
# meaningful message
assert "Encountered exception in stationarity test" in pytest_error_str(ve)
def test_max_dur():
# set arbitrarily low to guarantee will always pass after one iter
with StepwiseContext(max_dur=.5), \
pytest.warns(UserWarning) as uw:
auto_arima(lynx, stepwise=True)
# assert that max_dur was reached
assert any(str(w.message).startswith('early termination') for w in uw)
def test_auto_arima_with_stepwise_context():
samp = lynx[:8]
with StepwiseContext(max_steps=3, max_dur=30):
with pytest.warns(UserWarning) as uw:
auto_arima(samp,
suppress_warnings=False,
stepwise=True,
error_action='ignore')
# assert that max_steps were taken
assert any(
str(w.message).startswith('stepwise search has reached the '
'maximum number of tries')
for w in uw)
def test_subsequent_contexts():
# Force a very fast fit
with StepwiseContext(max_dur=.5), \
pytest.warns(UserWarning):
auto_arima(lynx, stepwise=True)
# Out of scope, should be EMPTY
assert ContextStore.get_or_empty(ContextType.STEPWISE).get_type() \
is ContextType.EMPTY
# Now show that we DON'T hit early termination by time here
with StepwiseContext(max_steps=100), \
pytest.warns(UserWarning) as uw:
ctx = ContextStore.get_or_empty(ContextType.STEPWISE)
assert ctx.get_type() is ContextType.STEPWISE
assert ctx.max_dur is None
auto_arima(lynx, stepwise=True)
# assert that max_dur was NOT reached
assert not any(
str(w.message).startswith('early termination') for w in uw)
#print(result)
result.plot()
plt.savefig('SeasonalDecompose.png')
#print(weeklySales.values)
#print(monthlySales.index)
#ap.plotacf(weeklySales,'WeeklySales',show=False,save=True)
#ap.plotacf(monthlySales,'MonthlylySales',show=False,save=True)
stepwise_model = auto_arima(dailySales,
start_p=1,
start_q=1,
max_p=3,
max_q=3,
m=7,
start_P=0,
seasonal=True,
d=1,
D=1,
trace=False,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
#print(stepwise_model.aic())
#print(stepwise_model.order)
#print(stepwise_model.seasonal_order)
model = sm.tsa.statespace.SARIMAX(dailySales,
order=stepwise_model.order,
seasonal_order=stepwise_model.seasonal_order,
enforce_stationarity=False,
enforce_invertibility=False)