This function conducts expanding window forecasting.
Usage
forecast_expand(object, n_ahead, y_test, num_thread = 1, ...)
# S3 method for class 'olsmod'
forecast_expand(object, n_ahead, y_test, num_thread = 1, ...)
# S3 method for class 'normaliw'
forecast_expand(object, n_ahead, y_test, num_thread = 1, use_fit = TRUE, ...)
# S3 method for class 'ldltmod'
forecast_expand(
object,
n_ahead,
y_test,
num_thread = 1,
level = 0.05,
sparse = FALSE,
lpl = FALSE,
use_fit = TRUE,
...
)
# S3 method for class 'svmod'
forecast_expand(
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
- ...
Additional arguments.
- use_fit
- level
Specify alpha of confidence interval level 100(1 - alpha) percentage. By default, .05.
- sparse
- lpl
- use_sv
Use SV term
Value
predbvhar_expand
class
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/