Sorry about my delayed answer, I was learning how to do it, follow reprex as requested:
#> Warning: package 'forecast' was built under R version 3.6.2
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
#> Warning: package 'tsibble' was built under R version 3.6.2
#> Warning: package 'fable' was built under R version 3.6.2
#> Carregando pacotes exigidos: fabletools
#> Warning: package 'fabletools' was built under R version 3.6.2
#> Attaching package: 'fabletools'
#> The following objects are masked from 'package:forecast':
#> accuracy, forecast
#> Warning: package 'distributional' was built under R version 3.6.2
iniciativa <- tibble(
data_planejada = sample(seq(as.Date("2020-01-01"), length=20, by="1 day"), size=20),
n = sample(seq(100), size=20)
iniciativa <- iniciativa %>% as_tsibble()
#> Using `data_planejada` as index variable.
ens_fit <- iniciativa %>%
tslm = TSLM(n ~ trend() + season(period = 22)),
arima = ARIMA(n ~ trend() + season(period = 3), stepwise = TRUE)
n = dist_mixture(
weights = c(0.5, 0.5))
#> Error in summarize(., n = dist_mixture(n, n, weights = c(0.5, 0.5))): não foi possível encontrar a função "summarize"
Created on 2020-11-11 by the reprex package (v0.3.0)
Also, I would like to hear some advice about how to choose the best model given the metrics attached.
There is a standard about when some error is acceptable than other ?
Thanks for your help