The states most likely to be subject to total revisions requiring substitution.
nhsn_recent_archive$DT %<>% mutate(geo_value = factor(geo_value, levels = av_re_spread$geo_value))
nhsn_recent_archive %>%
autoplot("value") + facet_wrap(~geo_value, ncol = 3, scales = "free") + theme(strip.text.x = element_text(size = 8)) +
labs(title = "States with the largest mean revision")
plotting_forecasts(plotting_window = Sys.Date() - 12 * 7, score_window = 8, n_plotting = 60)