ARIMA.predict()

statsmodels.tsa.arima_model.ARIMA.predict

ARIMA.predict(params, start=None, end=None, exog=None, typ='linear', dynamic=False) [source]

ARIMA model in-sample and out-of-sample prediction

Parameters:

params : array-like The fitted parameters of the model. start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. However, if the dates index does not have a fixed frequency, end must be an integer index if you want out of sample prediction. exog : array-like, optional If the model is an ARMAX and out-of-sample forecasting is requested, exog must be given. Note that you’ll need to pass k_ar additional lags for any exogenous variables. E.g., if you fit an ARMAX(2, q) model and want to predict 5 steps, you need 7 observations to do this. dynamic : bool, optional The dynamic keyword affects in-sample prediction. If dynamic is False, then the in-sample lagged values are used for prediction. If dynamic is True, then in-sample forecasts are used in place of lagged dependent variables. The first forecasted value is start. typ : str {‘linear’, ‘levels’} ‘linear’ : Linear prediction in terms of the differenced endogenous variables. ‘levels’ : Predict the levels of the original endogenous variables.

Returns:

predict : array The predicted values.

Notes

Use the results predict method instead.