statsmodels.regression.recursive_ls.RecursiveLSResults.append¶
- RecursiveLSResults.append(endog, exog=None, refit=False, fit_kwargs=None, copy_initialization=False, **kwargs)¶
Recreate the results object with new data appended to the original data
Creates a new result object applied to a dataset that is created by appending new data to the end of the model’s original data. The new results can then be used for analysis or forecasting.
- Parameters:
- endogarray_like
New observations from the modeled time-series process.
- exogarray_like,
optional
New observations of exogenous regressors, if applicable.
- refitbool,
optional
Whether to re-fit the parameters, based on the combined dataset. Default is False (so parameters from the current results object are used to create the new results object).
- copy_initializationbool,
optional
Whether or not to copy the initialization from the current results set to the new model. Default is False
- fit_kwargs
dict
,optional
Keyword arguments to pass to fit (if refit=True) or filter / smooth.
- copy_initializationbool,
optional
- **kwargs
Keyword arguments may be used to modify model specification arguments when created the new model object.
- Returns:
results
Updated Results object, that includes results from both the original dataset and the new dataset.
See also
Notes
The endog and exog arguments to this method must be formatted in the same way (e.g. Pandas Series versus Numpy array) as were the endog and exog arrays passed to the original model.
The endog argument to this method should consist of new observations that occurred directly after the last element of endog. For any other kind of dataset, see the apply method.
This method will apply filtering to all of the original data as well as to the new data. To apply filtering only to the new data (which can be much faster if the original dataset is large), see the extend method.
Examples
>>> index = pd.period_range(start='2000', periods=2, freq='A') >>> original_observations = pd.Series([1.2, 1.5], index=index) >>> mod = sm.tsa.SARIMAX(original_observations) >>> res = mod.fit() >>> print(res.params) ar.L1 0.9756 sigma2 0.0889 dtype: float64 >>> print(res.fittedvalues) 2000 0.0000 2001 1.1707 Freq: A-DEC, dtype: float64 >>> print(res.forecast(1)) 2002 1.4634 Freq: A-DEC, dtype: float64
>>> new_index = pd.period_range(start='2002', periods=1, freq='A') >>> new_observations = pd.Series([0.9], index=new_index) >>> updated_res = res.append(new_observations) >>> print(updated_res.params) ar.L1 0.9756 sigma2 0.0889 dtype: float64 >>> print(updated_res.fittedvalues) 2000 0.0000 2001 1.1707 2002 1.4634 Freq: A-DEC, dtype: float64 >>> print(updated_res.forecast(1)) 2003 0.878 Freq: A-DEC, dtype: float64