Internal helper function to get the prediction type of a
forecast. That is inferred based on the properties of the values in the
prediction column.
Arguments
- data
A data.frame or data.table with the predictions and observations. For scoring using
score(), the following columns need to be present:true_value- the true observed valuesprediction- predictions or predictive samples for one true value. (You only don't need to provide a prediction column if you want to score quantile forecasts in a wide range format.)
For scoring integer and continuous forecasts a
samplecolumn is needed:sample- an index to identify the predictive samples in the prediction column generated by one model for one true value. Only necessary for continuous and integer forecasts, not for binary predictions.
For scoring predictions in a quantile-format forecast you should provide a column called
quantile:quantile: quantile to which the prediction corresponds
In addition a
modelcolumn is suggested and if not present this will be flagged and added to the input data with all forecasts assigned as an "unspecified model").You can check the format of your data using
check_forecasts()and there are examples for each format (example_quantile, example_continuous, example_integer, and example_binary).