predict.tfm: Forecast Transfer Function Model

In tfarima: Transfer Function and ARIMA Models

Forecast Transfer Function Model

Description

Computes point forecasts and prediction intervals for transfer function models.

Usage

## S3 method for class 'tfm'
predict(
  object,
  newdata = NULL,
  y = NULL,
  ori = NULL,
  n.ahead = NULL,
  level = 0.95,
  i = NULL,
  envir = NULL,
  ...
)

Arguments

object	A fitted tfm object.
newdata	Optional matrix or vector of future values for exogenous regressors and inputs. Rows correspond to forecast horizon, columns to predictors.
y	Optional ts object for alternative output series.
ori	Forecast origin (observation index). Default is last observation.
n.ahead	Number of steps ahead to forecast. Default is series frequency.
level	Confidence level(s) for prediction intervals (0-1). Default is 0.95. Can be a vector for multiple intervals.
i	Optional differencing operator (lagpol) to apply before forecasting.
envir	Environment for evaluation. NULL uses calling environment.
...	Additional arguments (currently unused).

Details

Future values for transfer function inputs can be provided in three ways: (1) extending input series beyond output length, (2) automatic forecasting from associated um models, or (3) via the newdata argument.

If Box-Cox transformation was used, forecasts are back-transformed and intervals adjusted accordingly.

Value

Object of class predict.tfm containing:

z	Complete series including forecasts
rmse	Root mean square error for each forecast
low, upp	Lower and upper prediction interval bounds (matrices)
level	Confidence level(s) used
dates	Time points for all observations
ori, ori.date	Forecast origin (index and date)
n.ahead	Number of forecasts

See Also

tfm, fit.tfm

tfarima documentation built on Nov. 5, 2025, 7:43 p.m.