ATAforecasting (version 0.0.60)

ATA.Accuracy: Accuracy Measures for The ATAforecasting

Description

Returns ATA(p,q,phi)(E,T,S) applied to `ata` object. Accuracy measures for a forecast model Returns range of summary measures of the forecast accuracy. If out.sample is provided, the function measures test set forecast accuracy. If out.sample is not provided, the function only produces training set accuracy measures. The measures calculated are:

• lik : maximum likelihood functions

• sigma : residual variance.

• MAE : mean absolute error.

• MSE : mean square error.

• RMSE : root mean squared error.

• MPE : mean percentage error.

• MAPE : mean absolute percentage error.

• sMAPE : symmetric mean absolute percentage error.

• MASE : mean absolute scaled error.

• OWA : overall weighted average of MASE and sMAPE.

• MdAE : median absolute error.

• MdSE : median square error.

• RMdSE : root median squared error.

• MdPE : median percentage error.

• MdAPE : median absolute percentage error.

• sMdAPE : symmetric median absolute percentage error.

Usage

ATA.Accuracy(object, out.sample = NULL, print.out = TRUE)

Value

Matrix giving forecast accuracy measures.

Arguments

object

An object of class ata is required.

out.sample

A numeric vector or time series of class ts or msts for out-sample.

print.out

Default is TRUE. If FALSE, summary of ATA Method's accuracy measures is not shown.

Author

Ali Sabri Taylan and Hanife Taylan Selamlar

References

#'hyndmanandkoehler2006ATAforecasting

#'hyndman2019forecastingATAforecasting

See Also

forecast, stlplus, stR, stl, decompose, tbats, seasadj.

Examples

trainATA <-  head(touristTR, 84)
testATA <- window(touristTR, start = 2015, end = 2016.917)
ata_fit <- ATA(trainATA, h=24, seasonal.test = TRUE, seasonal.model = "decomp")
ata_accuracy <- ATA.Accuracy(ata_fit, testATA)