# statsmodels.tsa.forecasting.theta.ThetaModelResults.prediction_intervals¶

ThetaModelResults.prediction_intervals(steps: int = 1, theta: float = 2, alpha: float = 0.05) → pandas.core.frame.DataFrame[source]
Parameters
stepsint, default 1

The number of steps ahead to compute the forecast components.

thetafloat, default 2

The theta value to use when computing the weight to combine the trend and the SES forecasts.

alphafloat, default 0.05

Significance level for the confidence intervals.

Returns
DataFrame

DataFrame with columns lower and upper

Notes

The variance of the h-step forecast is assumed to follow from the integrated Moving Average structure of the Theta model, and so is $$\sigma^2(1 + (h-1)(1 + (\alpha-1)^2)$$. The prediction interval assumes that innovations are normally distributed.