Hi there,

I need to one-step-ahead cross-validate in list of models. But I am struggling with `modeltime_resample_accuracy`

.

My idea is to create a function to cross-validate a list of model. But before that, I was trying to understand how `modeltime_resample_accuracy`

works, and I got a error:

```
Error: In metric: `mase`
Problem with `summarise()` column `.estimate`.
i `.estimate = metric_fn(...)`.
x `truth` must have a length greater than `m` to compute the out-of-sample naive mean absolute error.
i The error occurred in group 1: .model_id = 1, .model_desc = "LM", .resample_id = "Slice1", .type = "Resamples".
Run `rlang::last_error()` to see where the error occurred.
```

I was running the following code:

```
library(tidyverse)
library(tidymodels)
library(modeltime)
library(modeltime.resample)
library(timetk)
library(lubridate)
library(zoo)
library(fpp3)
list_of_regressors <- c(1, us_change[3:6] %>% colnames())
regressor_combination <- function(.indepent_var,
.regressors = c(""),
.min_regs = 1,
.max_regs = length(.regressors)) {
regressors_list <- map(.x = .min_regs:.max_regs,
.f = ~ combn(x = .regressors,
m = .x) %>%
split(x = .,
f = rep(1:ncol(.), each = nrow(.)))) %>%
do.call(what = c,
args = .) %>%
unname()
output <- tibble(
regressors = regressors_list
) %>%
mutate(id = row_number()) %>%
select(id, everything()) %>%
mutate(formula = map(.x = regressors,
~ paste0(.indepent_var, " ~ ", paste(.x, collapse = " + "))) %>%
unlist())
return(output)
}
tbl_models <- regressor_combination(.indepent_var = "Consumption",
.regressors = list_of_regressors)
data <- us_change %>%
as_tibble()
train_data <- training(initial_time_split(data = data, prop = 0.8))
test_data <- testing(initial_time_split(data = data, prop = 0.8))
resamples_tscv <- time_series_cv(
data = data,
date_var = Quarter,
assess = 1,
initial = nrow(data)-4*2
)
model <- linear_reg() %>%
set_engine("lm") %>%
fit(as.formula(tbl_models$formula[11]), data = train_data)
modeltime_table(model) %>%
modeltime_fit_resamples(
resamples = resamples_tscv,
control = control_resamples(verbose = FALSE)
) %>%
modeltime_resample_accuracy()
```