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.
ATA.Accuracy(object, out.sample = NULL, print.out = TRUE)Arguments
- object
An object of class
atais required.- out.sample
A numeric vector or time series of class
tsormstsfor out-sample.- print.out
Default is TRUE. If FALSE, summary of ATA Method's accuracy measures is not shown.
Value
Matrix giving forecast accuracy measures.
References
#'Hyndman RJ, Koehler AB (2006). “Another look at measures of forecast accuracy.” International Journal of Forecasting, 22(4), 679--688.
#'Hyndman RJ, Athanasopoulos G (2019). Forecasting: principles and practice. OTexts. https://otexts.com/fpp3/.
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(2,2,1) (A,M,M)
#>
#> model.type: M
#>
#> seasonal.model: decomp
#>
#> seasonal.type: M
#>
#> forecast horizon: 24
#>
#> accuracy.type: sMAPE
#>
#> Model Fitting Measures:
#>
#> sigma2 loglik MAE
#> 25327678645.19596481 -1188.55670094 106527.45549230
#> MSE RMSE MPE
#> 23496762116.62757874 153286.53599266 0.12753452
#> MAPE sMAPE MASE
#> 4.04068178 4.04508846 0.23313082
#> OWA
#> 0.00000468
#>
#> In-Sample Accuracy Measures:
#>
#> MdAE MdSE RMdSE MdPE
#> 67333.7563803 4533834748.2874908 67333.7563803 0.4018348
#> MdAPE sMdAPE
#> 3.2125716 3.2275752
#>
#> Out-Sample Accuracy Measures:
#>
#> MAE MSE RMSE MPE MAPE sMAPE MASE OWA
#> NA NA NA NA NA NA NA NA
#>
#> Out-Sample Accuracy Measures:
#>
#> MdAE MdSE RMdSE MdPE MdAPE sMdAPE
#> NA NA NA NA NA NA
#>
#> Information Criteria:
#>
#> AIC AICc BIC
#> 2391.113 2392.587 2408.129
#>
#>
#> user system elapsed
#> 1.263 0.122 1.385
#>
#> calculation.time: 1.385
#>
#>
#> Forecasts:
#> Time Series:
#> Start = 2015.00694444444
#> End = 2016.92361111111
#> Frequency = 12
#> [1] 1288568 1211322 1716223 2292963 3589547 4283724 5018661 5887346 4933780
#> [10] 4253560 2170497 1480814 1363260 1281536 1815704 2425875 3797616 4532031
#> [19] 5309568 6228607 5219767 4500118 2296310 1566650
#>
#>
ata_accuracy <- ATA.Accuracy(ata_fit, testATA)
#> Model Fitting Measures:
#>
#> sigma2 loglik MAE
#> 25327678645.19596481 -1188.55670094 106527.45549230
#> MSE RMSE MPE
#> 23496762116.62757874 153286.53599266 0.12753452
#> MAPE sMAPE MASE
#> 4.04068178 4.04508846 0.23313082
#> OWA
#> 0.00000468
#>
#> Out-Sample Accuracy Measures:
#>
#> MAE MSE RMSE
#> 745266.93257922 1268723834847.31567383 1126376.41792046
#> MPE MAPE sMAPE
#> -28.69225078 29.56321404 23.01067141
#> MASE OWA
#> 1.63098511 0.00002695
#>
#>