statsmodels.tsa.base.prediction.PredictionResults

class statsmodels.tsa.base.prediction.PredictionResults(predicted_mean, var_pred_mean, dist=None, df=None, row_labels=None)

Prediction results

Parameters

predicted_mean{ndarray, Series, DataFrame}

The predicted mean values

var_pred_mean{ndarray, Series, DataFrame}

The variance of the predicted mean values

dist{None, “norm”, “t”, rv_frozen}

The distribution to use when constructing prediction intervals. Default is normal.

dfint, optional

The degree of freedom parameter for the t. Not used if dist is None, “norm” or a callable.

row_labels{Sequence[Hashable], pd.Index}

Row labels to use for the summary frame. If None, attempts to read the index of predicted_mean

Attributes

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

Methods

conf_int([alpha])	Confidence interval construction for the predicted mean.
summary_frame([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([alpha])	Confidence interval construction for the predicted mean.
summary_frame([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