statsmodels.tsa.arima.model.ARIMAResults.predict¶
- ARIMAResults.predict(start=None, end=None, dynamic=False, **kwargs)¶
In-sample prediction and out-of-sample forecasting
- Parameters:
- start{
int
, str,datetime},optional
Zero-indexed observation number at which to start forecasting, i.e., the first forecast is start. Can also be a date string to parse or a datetime type. Default is the zeroth observation.
- end{
int
, str,datetime},optional
Zero-indexed observation number at which to end forecasting, i.e., the last forecast is end. 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. Default is the last observation in the sample.
- dynamic{bool,
int
, str,datetime},optional
Integer offset relative to start at which to begin dynamic prediction. Can also be an absolute date string to parse or a datetime type (these are not interpreted as offsets). Prior to this observation, true endogenous values will be used for prediction; starting with this observation and continuing through the end of prediction, forecasted endogenous values will be used instead.
- **kwargs
Additional arguments may be required for forecasting beyond the end of the sample. See
FilterResults.predict
for more details.
- start{
- Returns:
- predictionsarray_like
In-sample predictions / Out-of-sample forecasts. (Numpy array or Pandas Series or DataFrame, depending on input and dimensions). Dimensions are (npredict x k_endog).
See also
forecast
Out-of-sample forecasts.
get_forecast
Out-of-sample forecasts and results including confidence intervals.
get_prediction
In-sample predictions / out-of-sample forecasts and results including confidence intervals.