Calculates the geometric mean root square error (GRMSE) between the forecast and the eventual outcomes.
X is the eventual outcome time series sample data (a one-dimensional array of cells e.g. row or column).
F is the forecast time series data (a one-dimensional array of cells e.g. row or column).
- 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:
- 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.
- R.J. Hyndman, A.B. Koehler, "Another look at measures of forecast accuracy", International Journal of Forecasting, 22 (2006), pp. 679-688
- 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: 0-471-690740
- D. S.G. Pollock; Handbook of Time Series Analysis, Signal Processing, and Dynamics; Academic Press; Har/Cdr edition(Nov 17, 1999), ISBN: 125609906