forecast_expand: Out-of-sample Forecasting based on Expanding Window

Description

This function conducts expanding window forecasting.

Usage

forecast_expand(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  ...
)
# S3 method for olsmod
forecast_expand(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  ...
)
# S3 method for normaliw
forecast_expand(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  use_fit = TRUE,
  ...
)
# S3 method for ldltmod
forecast_expand(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  stable = FALSE,
  sparse = FALSE,
  med = FALSE,
  lpl = FALSE,
  mcmc = TRUE,
  use_fit = TRUE,
  verbose = FALSE,
  ...
)
# S3 method for svmod
forecast_expand(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  use_sv = TRUE,
  stable = FALSE,
  sparse = FALSE,
  med = FALSE,
  lpl = FALSE,
  mcmc = TRUE,
  use_fit = TRUE,
  verbose = FALSE,
  ...
)

Value

predbvhar_expand

class

Arguments

object: Model object
n_ahead: Step to forecast in rolling window scheme
y_test: Test data to be compared. Use divide_ts() if you don't have separate evaluation dataset.
level: Specify alpha of confidence interval level 100(1 - alpha) percentage. By default, .05.
newxreg: New values for exogenous variables. Should have the same row numbers as y_test.
num_thread: Number of threads
...: Additional arguments.
use_fit: Use object result for the first window. By default, TRUE.
stable: Filter only stable coefficient draws in MCMC records.
sparse: Apply restriction. By default, FALSE.
med: If TRUE, use median of forecast draws instead of mean (default).
lpl: Compute log-predictive likelihood (LPL). By default, FALSE.
mcmc: If TRUE, run new MCMC in new windows. By default, TRUE.
verbose: Print the progress bar in the console. By default, FALSE.
use_sv: Use SV term

Details

Expanding windows forecasting fixes the starting period. It moves the window ahead and forecast h-ahead in y_test set.

References

Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTEXTS. https://otexts.com/fpp3/