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Evaluate forecasts in the directory, based on id(s).

Current metrics include raw error (which can be used to calculate root mean squared error; RMSE), coverage, log score, and continuous rank probability score (CRPS).
read_forecasts_evaluations read in the forecasts evaluations file.


evaluate_forecasts(main = ".", forecasts_ids = NULL)

evaluate_forecast(main = ".", forecast_id = NULL)

read_forecasts_evaluations(main = ".")



character value of the name of the main component of the directory tree.

forecast_id, forecasts_ids

integer (or integer numeric) value(s) representing the forecasts of interest for evaluating, as indexed within the forecasts subdirectory. See the forecasts metadata file (forecasts_metadata.csv) for summary information.
forecast_id can only be a single value, whereas forecasts_ids can be multiple.


A data.frame of all forecast evaluations at the observation (newmoon) level, as requested, invisible-ly.

See also

Core forecasting functions: ensemble, portalcast(), process forecast output


if (FALSE) {
   main1 <- file.path(tempdir(), "evaluations")
   setup_dir(main = main1)

   plot_covariates(main = main1)

   make_model_combinations(main = main1)

   portalcast(main   = main1, 
              models = "AutoArima")

   cast(main    = main1,
        model   = "AutoArima", 
        dataset = "controls", 
        species = "DM")

   ## evaluate_forecasts(main = main1) ## extensive runtime for full evaluation from scratch

   ids <- select_forecasts(main = main1)$forecast_id
   evaluate_forecast(main        = main1, 
                     forecast_id = ids[1])

   read_forecasts_evaluations(main = main1)

   unlink(main1, recursive = TRUE)