I get NA forecast when I apply `ARIMA()`

on Box-Cox transformed data (lamda = -0.9999242).

However, when I use log transformation, I no longer get NA forecast. What should I do in this case?

```
library(fpp3)
```

```
# DATA
```

```
v1 <- c(32, 30, 39, 34, 31, 31, 30.5, 34, 28, 34, 35, 35, 30.5, 31,
27, 33.5, 35, 28, 34, 35, 34, 39, 36, 33.5, 36, 33, 31, 39, 34.5,
34, 32.5, 30, 27.5, 27, 39.5, 38, 32.5, 34, 43, 34, 32, 43, 36,
41, 35.5, 39, 44, 42.5, 34, 36, 49, 35, 44, 36, 42, 40.5, 38.5,
33, 36, 33, 36.5, 43, 32, 35, 38.5, 42, 31, 43, 32.5, 34, 35.5,
35, 33, 29, 35, 42, 37, 39, 45, 36, 52, 38, 36, 41.5, 43, 31.5,
37, 47, 38, 50, 51, 41, 32, 40.5, 37, 39.5, 36, 36.5, 38.5, 38,
47.5, 39, 37, 34, 32, 36, 35, 41, 41, 39.5, 44, 44, 65.5, 38,
45, 34, 35, 32, 62, 54.5)
# tsibble
tibble(
year = rep(2008:2017, each = 12),
m = month(rep(1:12, times = 10), label = TRUE),
toy_variable = v1,
month = yearmonth(paste(year, m)),
index = month
)%>%
select(month, toy_variable) %>%
as_tsibble(index = month) -> toy_data
# lambda
lambda_toy <- toy_data %>%
features(toy_variable, features = guerrero) %>%
pull(lambda_guerrero)
```

```
#########################################
# Auto ARIMA: lambda = -0.9999242
#####################################
toy_data %>%
model(ARIMA(box_cox(toy_variable, lambda_toy))) %>%
forecast()
#> # A fable: 24 x 4 [1M]
#> # Key: .model [1]
#> .model month toy_variable .mean
#> <chr> <mth> <dist> <dbl>
#> 1 ARIMA(box_cox(toy_variable, lambda_toy)) 2018 Jan t(N(0.98, 1.4e-05)) NA
#> 2 ARIMA(box_cox(toy_variable, lambda_toy)) 2018 Feb t(N(0.98, 1.4e-05)) NA
#> 3 ARIMA(box_cox(toy_variable, lambda_toy)) 2018 Mar t(N(0.98, 1.4e-05)) NA
#> 4 ARIMA(box_cox(toy_variable, lambda_toy)) 2018 Apr t(N(0.98, 1.4e-05)) NA
#> 5 ARIMA(box_cox(toy_variable, lambda_toy)) 2018 May t(N(0.98, 1.5e-05)) NA
#> 6 ARIMA(box_cox(toy_variable, lambda_toy)) 2018 Jun t(N(0.98, 1.5e-05)) NA
#> 7 ARIMA(box_cox(toy_variable, lambda_toy)) 2018 Jul t(N(0.98, 1.5e-05)) NA
#> 8 ARIMA(box_cox(toy_variable, lambda_toy)) 2018 Aug t(N(0.98, 1.5e-05)) NA
#> 9 ARIMA(box_cox(toy_variable, lambda_toy)) 2018 Sep t(N(0.98, 1.6e-05)) NA
#> 10 ARIMA(box_cox(toy_variable, lambda_toy)) 2018 Oct t(N(0.98, 1.6e-05)) NA
#> # ... with 14 more rows
######################################
# Auto ARIMA log-transformed data
#####################################
toy_data %>%
model(ARIMA(log(toy_variable))) %>%
forecast()
#> # A fable: 24 x 4 [1M]
#> # Key: .model [1]
#> .model month toy_variable .mean
#> <chr> <mth> <dist> <dbl>
#> 1 ARIMA(log(toy_variable)) 2018 Jan t(N(3.8, 0.02)) 43.3
#> 2 ARIMA(log(toy_variable)) 2018 Feb t(N(3.8, 0.021)) 43.4
#> 3 ARIMA(log(toy_variable)) 2018 Mar t(N(3.8, 0.021)) 43.4
#> 4 ARIMA(log(toy_variable)) 2018 Apr t(N(3.8, 0.022)) 43.4
#> 5 ARIMA(log(toy_variable)) 2018 May t(N(3.8, 0.022)) 43.4
#> 6 ARIMA(log(toy_variable)) 2018 Jun t(N(3.8, 0.022)) 43.4
#> 7 ARIMA(log(toy_variable)) 2018 Jul t(N(3.8, 0.023)) 43.4
#> 8 ARIMA(log(toy_variable)) 2018 Aug t(N(3.8, 0.023)) 43.4
#> 9 ARIMA(log(toy_variable)) 2018 Sep t(N(3.8, 0.023)) 43.4
#> 10 ARIMA(log(toy_variable)) 2018 Oct t(N(3.8, 0.024)) 43.4
#> # ... with 14 more rows
```

^{Created on 2020-08-03 by the reprex package (v0.3.0)}