mase.R

library(tidyverse)
# naive forecast method example
sales <- tibble(g = c(rep("insample", 23), rep("outsample", 12)),
       y = c(0,2,0,1,0,10,0,0,0,2,0,6,3,0,0,0,0,0,7,0,0,0,0, 0, 0, 0, 3, 1, 0, 0, 1, 0, 1, 0, 0),
       y_hat = ifelse(g == "insample", lag(y), last(y)),
       error = abs(y - y_hat))

sales

sales %>%
  slice(-1) %>% # to remove NA row
  mutate(denominator = mean(abs(y[g == "insample"] - y_hat[g == "insample"]), na.rm = TRUE)) %>%
  group_by(g) %>%
  summarise(mase = mean( abs( ( y - y_hat ) / denominator) ) )