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predict_arima

 predict_arima (model, n_ahead, newxreg=None, se_fit=True)

myarima(ap, order=(2, 1, 1), seasonal={'order': (0, 1, 0), 'period': 12}, 
        constant=False, ic='aicc', method='CSS-ML')['aic']
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arima_string

 arima_string (model, padding=False)
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forecast_arima

 forecast_arima (model, h=None, level=None, fan=False, xreg=None,
                 blambda=None, bootstrap=False, npaths=5000, biasadj=None)
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fitted_arima

 fitted_arima (model, h=1)

Returns h-step forecasts for the data used in fitting the model.

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auto_arima_f

 auto_arima_f (x, d=None, D=None, max_p=5, max_q=5, max_P=2, max_Q=2,
               max_order=5, max_d=2, max_D=1, start_p=2, start_q=2,
               start_P=1, start_Q=1, stationary=False, seasonal=True,
               ic='aicc', stepwise=True, nmodels=94, trace=False,
               approximation=None, method=None, truncate=None, xreg=None,
               test='kpss', test_kwargs=None, seasonal_test='seas',
               seasonal_test_kwargs=None, allowdrift=True, allowmean=True,
               blambda=None, biasadj=False, period=1)
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 print_statsforecast_ARIMA (model, digits=3, se=True)
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ARIMASummary

 ARIMASummary (model)

ARIMA Summary.

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AutoARIMA

 AutoARIMA (d:Optional[int]=None, D:Optional[int]=None, max_p:int=5,
            max_q:int=5, max_P:int=2, max_Q:int=2, max_order:int=5,
            max_d:int=2, max_D:int=1, start_p:int=2, start_q:int=2,
            start_P:int=1, start_Q:int=1, stationary:bool=False,
            seasonal:bool=True, ic:str='aicc', stepwise:bool=True,
            nmodels:int=94, trace:bool=False,
            approximation:Optional[bool]=None, method:Optional[str]=None,
            truncate:Optional[bool]=None, test:str='kpss',
            test_kwargs:Optional[str]=None, seasonal_test:str='seas',
            seasonal_test_kwargs:Optional[Dict]=None,
            allowdrift:bool=True, allowmean:bool=True,
            blambda:Optional[float]=None, biasadj:bool=False,
            period:int=1)

*An AutoARIMA estimator.

Returns best ARIMA model according to either AIC, AICc or BIC value. The function conducts a search over possible model within the order constraints provided.*