greybox (version 0.5.3)

measures: Error measures for the provided forecasts

Description

Function calculates several error measures using the provided forecasts and the data for the holdout sample.

Usage

measures(holdout, forecast, actual, digits = NULL)

Arguments

holdout

The vector of the holdout values.

forecast

The vector of forecasts produced by a model.

actual

The vector of actual in-sample values.

digits

Number of digits of the output. If NULL then no rounding is done.

Value

The functions returns the named vector of errors:

• MAE,

• MSE

• MPE,

• MAPE,

• MASE,

• sMAE,

• sMSE,

• sCE,

• rMAE,

• rRMSE,

• rAME,

• cbias,

• sPIS.

For the details on these errors, see Errors.

References

• Svetunkov, I. (2017). Naughty APEs and the quest for the holy grail. https://forecasting.svetunkov.ru/en/2017/07/29/naughty-apes-and-the-quest-for-the-holy-grail/

• Fildes R. (1992). The evaluation of extrapolative forecasting methods. International Journal of Forecasting, 8, pp.81-98.

• Hyndman R.J., Koehler A.B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22, pp.679-688.

• Petropoulos F., Kourentzes N. (2015). Forecast combinations for intermittent demand. Journal of the Operational Research Society, 66, pp.914-924.

• Wallstrom P., Segerstedt A. (2010). Evaluation of forecasting error measurements and techniques for intermittent demand. International Journal of Production Economics, 128, pp.625-636.

• Davydenko, A., Fildes, R. (2013). Measuring Forecasting Accuracy: The Case Of Judgmental Adjustments To Sku-Level Demand Forecasts. International Journal of Forecasting, 29(3), 510-522. https://doi.org/10.1016/j.ijforecast.2012.09.002

Examples

# NOT RUN {

y <- rnorm(100,10,2)
ourForecast <- rep(mean(y[1:90]),10)

measures(y[91:100],ourForecast,y[1:90],digits=5)

# }