Allows you to extract elements of interest from the model which can be useful in understanding how they contribute towards the overall fitted values.
# S3 method for class 'mdl_df'
components(object, ...)
# S3 method for class 'mdl_ts'
components(object, ...)A dable will be returned, which will allow you to easily plot the components and see the way in which components are combined to give forecasts.
The components can also be visualised using the autoplot() method provided
by the ggtime package.
library(fable)
library(tsibbledata)
# Forecasting with an ETS(M,Ad,A) model to Australian beer production
aus_production %>%
model(ets = ETS(log(Beer) ~ error("M") + trend("Ad") + season("A"))) %>%
components()
#> # A dable: 222 x 7 [1Q]
#> # Key: .model [1]
#> # : log(Beer) = (lag(level, 1) + 0.941163396605833 * lag(slope, 1) +
#> # lag(season, 4)) * (1 + remainder)
#> .model Quarter `log(Beer)` level slope season remainder
#> <chr> <qtr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ets 1955 Q1 NA NA NA 0.0492 NA
#> 2 ets 1955 Q2 NA NA NA -0.142 NA
#> 3 ets 1955 Q3 NA NA NA -0.0823 NA
#> 4 ets 1955 Q4 NA 5.54 0.00159 0.175 NA
#> 5 ets 1956 Q1 5.65 5.55 0.00477 0.0615 0.0111
#> 6 ets 1956 Q2 5.36 5.54 0.00191 -0.151 -0.00899
#> 7 ets 1956 Q3 5.42 5.54 -0.000262 -0.0900 -0.00713
#> 8 ets 1956 Q4 5.73 5.54 0.000554 0.178 0.00264
#> 9 ets 1957 Q1 5.57 5.54 -0.00139 0.0543 -0.00643
#> 10 ets 1957 Q2 5.43 5.54 0.00106 -0.143 0.00830
#> # ℹ 212 more rows