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)