make_accuracy: Estimate accuracy metrics to evaluate point forecast

In ahaeusser/tscv: Functions and utilities for tidy time series forecasting and time series cross-validation

Estimate accuracy metrics to evaluate point forecast

Description

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

Usage

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.

ahaeusser/tscv documentation built on July 26, 2023, 3:18 p.m.