statsmodels.tsa.arima_model.ARIMA.predict
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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. Ifdynamic
is True, then in-sample forecasts are used in place of lagged dependent variables. The first forecasted value isstart
.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.
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