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For sample-based multivariate forecasts, the default scoring rules are:
"energy_score" = energy_score_multivariate()
energy_score_multivariate()
"variogram_score" = variogram_score_multivariate()
variogram_score_multivariate()
# S3 method for forecast_multivariate_sample get_metrics(x, select = NULL, exclude = NULL, ...)
A forecast object (a validated data.table with predicted and observed values, see as_forecast_binary()).
as_forecast_binary()
A character vector of scoring rules to select from the list. If select is NULL (the default), all possible scoring rules are returned.
select
NULL
A character vector of scoring rules to exclude from the list. If select is not NULL, this argument is ignored.
unused
Overview of required input format for sample-based forecasts
Other get_metrics functions: get_metrics(), get_metrics.forecast_binary(), get_metrics.forecast_multivariate_point(), get_metrics.forecast_nominal(), get_metrics.forecast_ordinal(), get_metrics.forecast_point(), get_metrics.forecast_quantile(), get_metrics.forecast_sample(), get_metrics.scores()
get_metrics()
get_metrics.forecast_binary()
get_metrics.forecast_multivariate_point()
get_metrics.forecast_nominal()
get_metrics.forecast_ordinal()
get_metrics.forecast_point()
get_metrics.forecast_quantile()
get_metrics.forecast_sample()
get_metrics.scores()
example <- as_forecast_multivariate_sample( example_sample_continuous, joint_across = c("location", "location_name") ) get_metrics(example)
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