The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between value (Sample and population values) predicted by a model or an estimator and the values actually observed. Basically, the RMSD represents the sample standard deviation of the differences between predicted values and observed values. These individual differences are called residuals when the calculations are performed over the data sample that was used for estimation, and are called prediction errors when computed out-of-sample. The RMSD serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSD is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent.
The RMSD of predicted values for times t of a regression'sdependent variable is computed for n different predictions as the square root of the mean of the squares of the deviations:
In some disciplines, the RMSD is used to compare differences between two things that may vary, neither of which is accepted as the "standard". For example, when measuring the average difference between two time series and , the formula becomes
Normalized root-mean-square deviation
The normalized root-mean-square deviation or error (NRMSD or NRMSE) is the RMSD divided by the range of observed values of a variable being predicted, or:
The value is often expressed as a percentage, where lower values indicate less residual variance.
The coefficient of variation of the RMSD, CV(RMSD), or more commonly CV(RMSE), is defined as the RMSD normalized to the mean of the observed values: