Hi,

I'm trying to cross validate my models with stretch_tsibble to find the lowest RMSE, it is a daily data,

I'm little confuse about .init parameter in stretch_tsibble, which is the best to choose?

Also follow my minimal example where I found erros using Fourier e NANs in accuracy, some help her will be appreciated,

Regards.

```
library(tsibble)
#> Warning: package 'tsibble' was built under R version 3.6.2
library(lubridate)
#> Warning: package 'lubridate' was built under R version 3.6.2
#>
#> Attaching package: 'lubridate'
#> The following object is masked from 'package:tsibble':
#>
#> interval
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
library(dplyr)
#> Warning: package 'dplyr' was built under R version 3.6.2
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(fable)
#> Warning: package 'fable' was built under R version 3.6.2
#> Carregando pacotes exigidos: fabletools
iniciativa <- tibble(
data_planejada = sample(seq(as.Date("2020-01-01"), length=200, by="1 day"), size=200),
n = sample(seq(200), size=200)
) %>% as_tsibble()
#> Using `data_planejada` as index variable.
train <- iniciativa %>%
filter_index("2020-01-01" ~ "2020-05-29")
test <- iniciativa %>%
filter_index("2020-05-30" ~ .)
tsibble_cv <- train %>%
slice(1:(n() - 140)) %>%
stretch_tsibble(.init = 2,
.step = 1)
fc_cv <- tsibble_cv %>%
model(
arima = ARIMA(n ~ trend() + PDQ(0,0,0) + fourier(K = 3)),
) %>%
forecast(h = "20 weeks")
#> Warning: Provided exogenous regressors are rank deficient, removing regressors:
#> `fourier(K = 3)S1_7`, `fourier(K = 3)C2_7`, `fourier(K = 3)S2_7`, `fourier(K =
#> 3)C3_7`, `fourier(K = 3)S3_7`
#> Warning: It looks like you're trying to fully specify your ARIMA model but have not said if a constant should be included.
#> You can include a constant using `ARIMA(y~1)` to the formula or exclude it by adding `ARIMA(y~0)`.
#> Warning: Provided exogenous regressors are rank deficient, removing regressors:
#> `fourier(K = 3)C2_7`, `fourier(K = 3)S2_7`, `fourier(K = 3)C3_7`, `fourier(K =
#> 3)S3_7`
#> Warning: Provided exogenous regressors are rank deficient, removing regressors:
#> `fourier(K = 3)S2_7`, `fourier(K = 3)C3_7`, `fourier(K = 3)S3_7`
#> Warning: Provided exogenous regressors are rank deficient, removing regressors:
#> `fourier(K = 3)C3_7`, `fourier(K = 3)S3_7`
#> Warning: Provided exogenous regressors are rank deficient, removing regressors:
#> `fourier(K = 3)S3_7`
#> Warning: 6 errors (1 unique) encountered for arima
#> [6] Could not find an appropriate ARIMA model.
#> This is likely because automatic selection does not select models with characteristic roots that may be numerically unstable.
#> For more details, refer to https://otexts.com/fpp3/arima-r.html#plotting-the-characteristic-roots
fc_cv %>%
accuracy(test)
#> Warning: The future dataset is incomplete, incomplete out-of-sample data will be treated as missing.
#> 148 observations are missing between 2020-01-03 and 2020-05-29
#> # A tibble: 1 x 10
#> .model .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 arima Test NaN NaN NaN NaN NaN NaN NaN NA
```

^{Created on 2020-12-07 by the reprex package (v0.3.0)}