predict-s_semiarma-method: Point and Interval Forecasts for Seasonal Semi-ARMA Models

In deseats: Data-Driven Locally Weighted Regression for Trend and Seasonality in TS

Point and Interval Forecasts for Seasonal Semi-ARMA Models

Description

Obtain point and interval forecasts based on fitted Seasonal Semi-ARMA models.

Usage

## S4 method for signature 's_semiarma'
predict(
  object,
  n.ahead = 1,
  intervals = TRUE,
  alpha = c(0.95, 0.99),
  method = c("norm", "boot"),
  bootMethod = c("simple", "advanced"),
  npaths = 5000,
  quant.type = 8,
  expo = FALSE,
  ...
)

Arguments

object	an object of class "s_semiarma".
n.ahead	a numeric vector of length one that represents the forecasting horizon; assume that object is based on observations at time points t=1,2,\dots,n; forecasts are then obtained for time points t=n+1,n+2,\dots,n+\code{n.ahead}; the default is n.ahead = 1.
intervals	a logical vector of length one that indicates whether or not forecasting intervals should be obtained; the default is intervals = TRUE.
alpha	a numeric vector of variable length that indicates the confidence levels at which to obtain forecasting intervals; the default is alpha = c(0.95, 0.99), i.e. confidence levels of 95 and 99 percent.
method	a character vector that indicates the method used to obtain forecasting intervals; available are theoretical intervals based on the assumption of normal innovations ("norm") and intervals through a bootstrap ("boot"); the default is method = "norm".
bootMethod	a character vector that allows the user to select a bootstrap procedure for the forecasting intervals when method = "boot" is selected; the default bootMethod = "simple" simulates future observations by resampling the obtained residuals; the second approach bootMethod = "advanced" also considers the variation in the ARMA coefficient estimates by simulating and reestimating complete ARMA paths upon which forecasts are obtained (see also the B-ARMARoots algorithm in Lu and Wang, 2020); the second approach is often time-consuming.
npaths	the number of paths to simulate, if the forecasting intervals are obtained via a bootstrap.
quant.type	the method to obtain sample quantiles from the simulated forecasting errors; see also the argument type of the function quantile.
expo	a logical vector of length one; indicates whether the forecasting results should be exponentiated at the end; the default is expo = FALSE.
...	only for comparability with the standard predict method.

Details

Assume a Seasonal Semi-ARMA model was fitted using s_semiarma. Pass the resulting object to this method, in order to obtain point and interval forecasts.

Value

A list with the following elements is returned.

pred

the obtained point forecasts.

interv

the obtained forecasting intervals.

obs

the observation series.

ts_name

the name of the observation series object.

Author(s)

• Dominik Schulz (Research Assistant) (Department of Economics, Paderborn University),
  Author and Package Creator

Examples

xt <- log(EXPENDITURES)
est <- s_semiarma(xt)
predict(est, n.ahead = 10)

deseats documentation built on Sept. 11, 2024, 8:24 p.m.