Closed dshemetov closed 6 months ago
TL;DR: target_date results in NA forecasts? If you comment out target_date and uncomment ahead lines below, the forecasts are reasonable.
target_date
ahead
r$> library(epidatr) # Access Delphi API library(epipredict) library(dplyr) format_storage <- function(pred, true_forecast_date, target_end_date) { pred %>% mutate( forecast_date = true_forecast_date, .dstn = nested_quantiles(.pred_distn) ) %>% unnest(.dstn) %>% select(-any_of(c(".pred_distn", ".pred", "time_value"))) %>% rename(quantile = quantile_levels, value = values, target_end_date = target_date) %>% relocate(geo_value, forecast_date, target_end_date, quantile, value) } epidata <- pub_covidcast( source = "jhu-csse", signals = "deaths_incidence_num", time_type = "day", geo_type = "state", geo_values = c("ca", "tx", "fl"), time_values = epirange(20210101, 20211231) ) %>% select(geo_value, time_value, deaths = value) %>% as_epi_df() fit <- flatline_forecaster( epidata, outcome = "deaths", args_list = flatline_args_list( # ahead = 1L, n_training = 7L, forecast_date = as.Date("2021-12-31"), target_date = as.Date("2022-01-01"), quantile_levels = c(0.2, 0.5, 0.8), ) ) format_storage(fit$predictions, as.Date("2021-12-31"), as.Date("2022-01-0 1")) # A tibble: 9 × 5 geo_value forecast_date target_end_date quantile value <chr> <date> <date> <dbl> <dbl> 1 ca 2021-12-31 2022-01-08 0.2 NA 2 ca 2021-12-31 2022-01-08 0.5 NA 3 ca 2021-12-31 2022-01-08 0.8 NA 4 fl 2021-12-31 2022-01-08 0.2 NA 5 fl 2021-12-31 2022-01-08 0.5 NA 6 fl 2021-12-31 2022-01-08 0.8 NA 7 tx 2021-12-31 2022-01-08 0.2 NA 8 tx 2021-12-31 2022-01-08 0.5 NA 9 tx 2021-12-31 2022-01-08 0.8 NA
TL;DR:
target_date
results in NA forecasts? If you comment outtarget_date
and uncommentahead
lines below, the forecasts are reasonable.