Decomposition modelling

This function allows you to specify a decomposition combination model using any additive decomposition. It works by first decomposing the data using the decomposition method provided to dcmp_fn with the given formula. Secondary models are used to fit each of the components from the resulting decomposition. These models are specified after the decomposition formula. All non-seasonal decomposition components must be specified, and any unspecified seasonal components will be forecasted using seasonal naive. These component models will be combined according to the decomposition method, giving a combination model for the response of the decomposition.

decomposition_model(dcmp, ...)

Arguments

dcmp

A model definition which supports extracting decomposed components().

...

Model definitions used to model the components

See also

Forecasting: Principles and Practice - Forecasting Decomposition

Examples

library(fable)
library(feasts)
library(tsibble)
library(dplyr)

vic_food <- tsibbledata::aus_retail %>% 
  filter(State == "Victoria", Industry == "Food retailing")
  
# Identify an appropriate decomposition
vic_food %>% 
  model(STL(log(Turnover) ~ season(window = Inf))) %>% 
  components() %>% 
  autoplot()

  
# Use an ETS model to seasonally adjusted data, and SNAIVE to season_year
# Any model can be used, and seasonal components will default to use SNAIVE.
my_dcmp_spec <- decomposition_model(
  STL(log(Turnover) ~ season(window = Inf)),
  ETS(season_adjust ~ season("N")), SNAIVE(season_year)
)

vic_food %>%
  model(my_dcmp_spec) %>% 
  forecast(h="5 years") %>% 
  autoplot(vic_food)