X is the input data sample (one/two dimensional array of cells (e.g. rows or columns))

1. The input time series data may include missing values (e.g. #N/A, #VALUE!, #NUM!, empty cell), but they will not be included in the calculations.
2. The root mean square (RMS) is defined as follows for a set of $n$ values ${x_1,x_2,...,x_n}$:
   $$\mathrm{RMS}=\sqrt{\frac{x_1^2+x_2^2+\cdots +x_N^2}{N}} =\sqrt{\frac{\sum_{i=1}^N {x_i^2}}{N}} $$
   Where:
   
   • $x_i$ is the value of the i-th non-missing observation
   • $N$ is the number of non-missing observations in the input sample data
3. The root mean square (RMS) is a statistical measure of the magnitude of a varying quantity.
4. The root mean square (RMS) has an interesting relationship to the mean ($\bar{x}$) and the population standard deviation ($\sigma$), such that:
   $$\mathrm{RMS}^2=\bar{x}^2+\sigma^2$$

Example 1:

Files Examples

Related Links

• Wikipedia - Root mean square

References

• Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
• Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740