To construct the r.m.s. error, you first need to determine the residuals. Residuals are the difference between the actual values and the predicted values. I denoted them by yi(hat) - yi, where yi is the observed value for the ith observation and yi(hat) is the predicted value. They can be positive or negative as the predicted value under or over estimates the actual value. Squaring the residuals, averaging the squares, and taking the square root gives us the r.m.s error. You then use the r.m.s. error as a measure of the spread of the y values about the predicted y value.
Note - this model assumes the observed data is regularly spaced in time. If the spacing of observation times is not constant, please consider one of the following models: