statsmodels.tsa.arima_model.ARIMA.predict¶

ARIMA.
predict
(params, start=None, end=None, exog=None, typ='linear', dynamic=False)[source]¶ ARIMA model insample and outofsample prediction
Parameters:  params (arraylike) – The fitted parameters of the model.
 start (int, str, or datetime) – Zeroindexed 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) – Zeroindexed 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 (arraylike, optional) – If the model is an ARMAX and outofsample 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 insample prediction. If dynamic is False, then the insample lagged values are used for prediction. If dynamic is True, then insample 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 – The predicted values.
Return type: array
Notes
Use the results predict method instead.