calculateMSEs: Mean square errors for the given data from dataset(MSEs)
In svcuonghvktqs/FORA: A package for time sereis forecast accuracy evaluation

Description Usage Arguments Value Author(s) References See Also Examples

This function calculates and returns list of two dataframes, where the first data frame contains MSEs for the given data, diferent horizons and methods, the second one contains ranked list of the methods according to MSEs. Also the function plots MSEs for different hirizons and methods.

1	calculateMSEs(frame, sort = FALSE)

`frame`	A data frame from dataset containing columns "actual", "forecast", "method" and "horizon".
`sort`	logical. If TRUE the resulting list of MSEs dataframe and ranked dataframe of MSEs sorting by average value.

calculateMAEs function calculates and returns list of two dataframes, where the first data frame contains MSEs for the given data, diferent horizons and methods, the second one contains ranked dataframe of the methods according to MSEs. Also the function plots MSEs for different hirizons and methods.

Sai Van Cuong, Maixm Shcherbakov and Andrey Davydenko

Rob J. Hyndman, Anne B. Koehler (2006) Volume title: "International Journal of Forecasting". Chapter title: Another look at measures of forecast accuracy. Chapter pages : (p.679-688). http://eva.fcea.edu.uy/pluginfile.php/109034/mod_resource/content/0/2006_Hyndman_Predicc.pdf.

calculateAvgRelMAEs, calculateGMAPEs, calculateGMRAEs, calculateMAD_MEAN_ratio, calculateMAEs, calculateMAPEs, calculateMASEs, calculateMdAPEs, calculateMPEs, calculatePB_MAEs, calculateRMSEs, calculateSMAPEs, calculateSMdAPEs.

calculateMSEs(frame = FORAYearForecast)
calculateMSEs(frame = FORAYearForecast, sort = TRUE)
data1 <- subset(FORAYearForecast, actual >= 5000| forecast < 8000)
data2 <- FORAYearForecast[1:300,]
calculateMSEs(frame = data1, sort = TRUE)
calculateMSEs(frame = data2, sort = TRUE)