Recent scores

Last 4 weeks

Last 8 weeks

Last 52 weeks

Moving 8 week average of scores

Mean WIS

Mean WIS per capita

Mean Coverage 90

Mean Coverage 50

Large recent data revisions

Large Mean Revision

The states most likely to be subject to total revisions requiring substitution.

All revisions

recent_archive$DT %<>% mutate(geo_value = factor(geo_value, levels = av_re_spread$geo_value))
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")

Forecasts from 8 weeks ago, sorted by decreasing recent WIS

Worst 5 by WIS per capita

Worst 5 by WIS

All by WIS per capita

plotting_forecasts(plotting_window = Sys.Date() - 12 * 7, score_window = 8, geo_score_rate_order, n_plotting = 60)

All by WIS

geo_score_order <- scores %>%
  filter(forecast_date > Sys.Date() - score_window * 7) %>%
  scores_by_state() %>%
  filter(forecaster == "CMU-TimeSeries") %>%
  arrange(desc(mean_wis)) %>% pull(geo_value)
plotting_forecasts(plotting_window = Sys.Date() - 12 * 7, score_window = 8, geo_score_order, n_plotting = 60)