Applies a fitted ETS model to a new dataset.
# S3 method for ETS
refit(
object,
new_data,
specials = NULL,
reestimate = FALSE,
reinitialise = TRUE,
...
)A model for which forecasts are required.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, the coefficients for the fitted model will be re-estimated to suit the new data.
If TRUE, the initial parameters will be re-estimated to suit the new data.
Other arguments passed to methods
lung_deaths_male <- as_tsibble(mdeaths)
lung_deaths_female <- as_tsibble(fdeaths)
fit <- lung_deaths_male %>%
model(ETS(value))
report(fit)
#> Series: value
#> Model: ETS(M,A,A)
#> Smoothing parameters:
#> alpha = 0.0002065548
#> beta = 0.0001865257
#> gamma = 0.000118306
#>
#> Initial states:
#> l[0] b[0] s[0] s[-1] s[-2] s[-3] s[-4] s[-5]
#> 1671.676 -4.334248 373.1746 -121.3157 -246.1697 -484.8581 -476.2192 -370.1939
#> s[-6] s[-7] s[-8] s[-9] s[-10] s[-11]
#> -303.5806 -207.384 122.0022 483.3319 620.3601 610.8525
#>
#> sigma^2: 0.009
#>
#> AIC AICc BIC
#> 1033.474 1044.807 1072.177
fit %>%
refit(lung_deaths_female, reinitialise = TRUE) %>%
report()
#> Series: value
#> Model: ETS(M,A,A)
#> Smoothing parameters:
#> alpha = 0.0002065548
#> beta = 0.0001865257
#> gamma = 0.000118306
#>
#> Initial states:
#> l[0] b[0] s[0] s[-1] s[-2] s[-3] s[-4] s[-5]
#> 586.8764 -0.7008449 129.4235 -60.401 -108.8126 -185.465 -189.2346 -149.2135
#> s[-6] s[-7] s[-8] s[-9] s[-10] s[-11]
#> -134.8698 -70.64105 45.28081 204.0216 279.4489 240.4628
#>
#> sigma^2: 0.0118
#>
#> AIC AICc BIC
#> 903.5169 910.8854 935.3903