I'm using non-linear regression (specially I'm using neural networks for time series prediction) and I'm a bit unsure about the calculation and interpretation of

*R^2*. Reading the forum I gather that

*R^2*has a different interpretation for non-linear regression and reading Wikipedia I see that there are several ways to calculate

*R^2*. I suspect that I can use the formula (take from Wikipedia):

**where**

*R^2*= 1 - SSerr/SStot*SSerr*is the sum of squares of residuals and

*SStot*is sum of all (

*observed values*-

*sample mean*)^2.

I'd really like to know if using the above formula is correct and how I can expect to interpret the

*R^2*value. I've read a few publications and mostly authors are quoting the mean absolute error and residual sum of squares... but quoting

*R^2*is important right (I hope at least that it's applicable and I haven't gone completely off target)?

Thank you for reading my post