Estimate accuracy metrics to evaluate point forecast

Source: R/make_accuracy.R

The function estimates several accuracy metrics to evaluate the accuracy of point forecasts. Either along the forecast horizon or along the test-splits. By default, the following accuracy metrics are provided:

  • ME: mean error

  • MAE: mean absolute error

  • MSE: mean squared error

  • RMSE: root mean squared error

  • MAPE: mean absolute percentage error

  • sMAPE: scaled mean absolute percentage error

  • MPE: mean percentage error

  • rMAE: relative mean absolute error

make_accuracy(
  future_frame,
  main_frame,
  context,
  dimension = "split",
  benchmark = NULL
)

Arguments

future_frame

A tibble containing the forecasts for the models, splits, etc.

main_frame

A tibble containing the actual values.

context

A named list with the identifiers for seried_id, value_id and index_id.

dimension

Character value. The forecast accuracy is estimated by split or horizon.

benchmark

Character value. The forecast model used as benchmark for the relative mean absolute error (rMAE).

Value

accuracy_frame is tibble containing the accuracy metrics.