Calculates the geometric mean root square error (GRMSE) between the forecast and the eventual outcomes.
Syntax
GRMSE(X, F)
X is the eventual outcome time series sample data (a onedimensional array of cells e.g. row or column).
F is the forecast time series data (a onedimensional array of cells e.g. row or column).
Remarks
 The time series is homogeneous or equally spaced.
 The time series X and F must be of identical size
 The time series X or F may include observations with missing values (e.g. #N/A or blank).
 Observations with missing values in Y or F are excluded from the GRMSE calculation.
 The geometric root mean squared error (GRMSE) is defined as follows:
$${\displaystyle{\mathrm{GRMSE}= \sqrt[2N]{\prod_{t=1}^{N}e_t^2}=\sqrt[2N]{\prod_{t=1}^{N}(y_tf_t)^2}}}$$  The GRMSE is more robust than RMSE and less affected by outliers.
 The main drawback is the scale dependency. If the forecast task includes objects with different scales or magnitudes then GRMSE measure could not applied.
 The GRMSE function is available starting with version 1.65 HAMMOCK.
Examples
Example 1:


Formula  Description (Result)  

=GRMSE($B$3:$B$21,$C$3:$C$21)  GRMSE (0.099035) 
Files Examples
References
 R.J. Hyndman, A.B. Koehler, "Another look at measures of forecast accuracy", International Journal of Forecasting, 22 (2006), pp. 679688
 James Douglas Hamilton; Time Series Analysis; Princeton University Press; 1st edition(Jan 11, 1994), ISBN: 691042896
 Tsay, Ruey S.; Analysis of Financial Time Series; John Wiley & SONS; 2nd edition(Aug 30, 2005), ISBN: 0471690740
 D. S.G. Pollock; Handbook of Time Series Analysis, Signal Processing, and Dynamics; Academic Press; Har/Cdr edition(Nov 17, 1999), ISBN: 125609906
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