--- title: ARIMA keywords: fastai sidebar: home_sidebar nb_path: "nbs/arima.ipynb" ---
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arima(ap, order=(2, 1, 1), seasonal={'order': (0, 1, 0), 'period': 12}, 
      include_mean=False, method='CSS-ML')['coef']
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predict_arima[source]

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

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predict_arima(res, 10)
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predict_arima(res_intercept, 10)
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newdrift = np.arange(ap.size + 1, ap.size + 10 + 1).reshape(-1, 1)
newxreg = np.concatenate([newdrift, np.sqrt(newdrift)], axis=1)
predict_arima(res_xreg, 10, newxreg=newxreg)
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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[source]

arima_string(model, padding=False)

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arima_string(res_Arima_ex)
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arima_string(res_Arima)
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forecast_arima[source]

forecast_arima(model, h=None, level=(80, 95), fan=False, xreg=None, blambda=None, bootstrap=False, npaths=5000, biasadj=None)

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auto_arima_f[source]

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, parallel=False, num_cores=2, period=1)

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mod = auto_arima_f(ap, period=12, method='CSS-ML', trace=True)
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