Out-of-sample Forecasting based on Rolling Window

This function conducts rolling window forecasting.

Usage

forecast_roll(object, n_ahead, y_test, num_thread = 1, ...)

# S3 method for class 'bvharcv'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.bvharcv(x)

# S3 method for class 'bvharcv'
knit_print(x, ...)

# S3 method for class 'olsmod'
forecast_roll(object, n_ahead, y_test, num_thread = 1, ...)

# S3 method for class 'normaliw'
forecast_roll(object, n_ahead, y_test, num_thread = 1, use_fit = TRUE, ...)

# S3 method for class 'ldltmod'
forecast_roll(
  object,
  n_ahead,
  y_test,
  num_thread = 1,
  level = 0.05,
  sparse = FALSE,
  lpl = FALSE,
  use_fit = TRUE,
  ...
)

# S3 method for class 'svmod'
forecast_roll(
  object,
  n_ahead,
  y_test,
  num_thread = 1,
  level = 0.05,
  use_sv = TRUE,
  sparse = FALSE,
  lpl = FALSE,
  use_fit = TRUE,
  ...
)

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.

num_thread

Number of threads

...

not used

x

bvharcv object

digits

digit option to print

use_fit

Use object result for the first window. By default, TRUE.

level

Specify alpha of confidence interval level 100(1 - alpha) percentage. By default, .05.

sparse

Apply restriction. By default, FALSE.

lpl

Compute log-predictive likelihood (LPL). By default, FALSE.

use_sv

Use SV term

Value

predbvhar_roll class

Details

Rolling windows forecasting fixes window size. 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.