statsmodels.tsa.statespace.mlemodel.PredictionResults#

class statsmodels.tsa.statespace.mlemodel.PredictionResults(model, prediction_results, row_labels=None, information_set='predicted', signal_only=False)[source]#

Prediction result from MLE models

Parameters:

modelMLEModel: The models used to make the prediction
prediction_resultskalman_filter.PredictionResults instance: Results object from prediction after fitting or filtering a state space model.
row_labelsiterable, optional: Row labels for the predicted data.
information_set{“predicted”, “filtered”, “smoothed”}, optional: Name of information set
signal_onlybool, optional: Whether the prediction is for the signal only

Attributes:

modelMLEModel: The models used to make the prediction
prediction_resultskalman_filter.PredictionResults instance: Results object from prediction after fitting or filtering a state space model.
information_setstr: Name of information set
signal_onlybool: Whether the prediction is for the signal only

Methods

`conf_int`([method, alpha])	Confidence interval construction for the predicted mean
`summary_frame`([endog, alpha])	Summary frame of mean, variance and confidence interval
`t_test`([value, alternative])	z- or t-test for hypothesis that mean is equal to value

Methods

`conf_int`([method, alpha])	Confidence interval construction for the predicted mean
`summary_frame`([endog, alpha])	Summary frame of mean, variance and confidence interval
`t_test`([value, alternative])	z- or t-test for hypothesis that mean is equal to value

Properties

`predicted_mean`	The predicted mean
`row_labels`	The row labels used in pandas-types
`se_mean`	The standard deviation of the predicted mean
`tvalues`	The ratio of the predicted mean to its standard deviation
`var_pred_mean`	The variance of the predicted mean