class-forecast-multivariate-point.md
In scoringutils: Utilities for Scoring and Assessing Predictions

as_forecast_multivariate_point() creates expected structure

Code
  cat("Class:", class(result), "\n")
Output
  Class: forecast_multivariate_point forecast data.table data.frame 
Code
  cat("Forecast type:", get_forecast_type(result), "\n")
Output
  Forecast type: multivariate_point 
Code
  cat("Forecast unit:", toString(get_forecast_unit(result)), "\n")
Output
  Forecast unit: location, model, target_type, target_end_date, horizon 
Code
  cat("Number of rows:", nrow(result), "\n")
Output
  Number of rows: 887 
Code
  cat("Number of columns:", ncol(result), "\n")
Output
  Number of columns: 8 
Code
  cat("Column names:", toString(names(result)), "\n")
Output
  Column names: observed, predicted, location, model, target_type, target_end_date, horizon, .mv_group_id 
Code
  cat("Number of unique groups:", length(unique(result$.mv_group_id)), "\n")
Output
  Number of unique groups: 224

score.forecast_multivariate_point() creates expected output

Code
  cat("Class:", class(scores), "\n")
Output
  Class: scores data.table data.frame 
Code
  cat("Number of rows:", nrow(scores), "\n")
Output
  Number of rows: 224 
Code
  cat("Number of columns:", ncol(scores), "\n")
Output
  Number of columns: 6 
Code
  cat("Column names:", toString(names(scores)), "\n")
Output
  Column names: model, target_type, target_end_date, horizon, variogram_score, .mv_group_id 
Code
  cat("Variogram score range:", paste(range(scores$variogram_score, na.rm = TRUE),
  collapse = " to "), "\n")
Output
  Variogram score range: 5.62526554714927 to 838304.038385032 
Code
  cat("Number of non-NA scores:", sum(!is.na(scores$variogram_score)), "\n")
Output
  Number of non-NA scores: 224