My question relates to the forecasting performance of 1- to many-step-ahead forecasts. An example from the FPP3 textbook section on time series cross-validation is shown below (with minimal edits for compactness). The example relates to 1- to 8-step-ahead drift forecasts.
library(fpp3) google_2015 <- gafa_stock %>% filter(Symbol == "GOOG", year(Date) >= 2015) %>% mutate(day = row_number()) %>% update_tsibble(index = day, regular = TRUE) %>% filter(year(Date) == 2015) google_2015_tr <- google_2015 %>% stretch_tsibble(.init = 3, .step = 1) fc <- google_2015_tr %>% model(RW(Close ~ drift())) %>% forecast(h = 8) %>% group_by(.id) %>% mutate(h = row_number()) %>% ungroup() fc %>% accuracy(google_2015) %>% select(.model,.type,RMSE,MAE,MAPE,MASE)
However, the number of observations in
google_2015 is 252. Shouldn't we be using
.init=51 to ensure we use at least 20% of the total number of observations as training data?