library(tidyverse)
library(tsibble) # for creating nicer representation of monthly data
#library(efor)
library(furrr) # for running the forecasting in parallel
library(forecast) #provides forecast mehotds
library(prophet)
library(rsample)
sales_data <- sales_monthly %>%
mutate(date = yearmonth(date))
splits <- sales_data %>%
filter(iterate == "Article_A") %>%
rsample::rolling_origin(
initial = 24,
assess = 6,
cumulative = TRUE,
skip = 5
)
analysis(splits$splits[[2]])
cv_forecasts <- splits %>%
split(.$id) %>%
purrr::map_df(tf_grouped_forecasts_cv, func = ets, n_pred = 6)
# https://tidymodels.github.io/rsample/articles/Applications/Time_Series.html
cv_forecasts %>%
ggplot(aes(x = date, y = y )) +
geom_line() +
facet_wrap(~split_id)
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