Forecasting future observations based off of lowest RMSE models

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My question pertains to what budugulo asked here Select models with lowest RMSE but I'm wondering how I can go further and use the models with the best predictive capability against the test data and apply it across the entire original hierarchical dataset to get future observations.

I understand how to forecast into the future with one individual time series, but I'm trying to forecast a hierarchical dataset that would require too much time to forecast the best models onto all of the original time series individually to forecast future observations. Is there a way to fit the best models (using lowest RMSE) onto the original time series in a hierarchical dataset to forecast future observations 3 years into the future (2020)?

Hopefully the code below will help towards answering my question.

library(tidyverse)
library(tsibble)
library(fable)
library(fpp3)


fit <- tourism %>%
  filter(Quarter <= yearquarter("2015 Q1")) %>%
  model(
    ets = ETS(Trips),
    arima = ARIMA(Trips)
  )

fc <- fit %>%
  forecast(new_data = filter(tourism, Quarter > yearquarter("2015 Q1")))

bestrmse <- accuracy(fc, tourism) %>%
  group_by(Region, State, Purpose) %>%
  filter(RMSE == min(RMSE)) %>%
  select(.model:Region)

bestfits <- fit %>%
  pivot_longer(cols=ets:arima, names_to = ".model", values_to = "fit") %>%
  right_join(bestrmse) %>%
  mutate(.model = "best") %>%
  pivot_wider(Region:Purpose, names_from = ".model", values_from = "fit") %>%
  as_mable(key = c(Region, State, Purpose), model = best)

#Apply 'best' models from bestfits onto original non-trained/non-tested time series and 
#forecast future observations into 2020.

Thank you.