# R-squared to compare forecasting techniques

Is it appropriate when forecasting to use $R^2$ as the measure of how well exponential smoothing fits a data set for the purpose of time-series forecasting?

I understand that it is appropriate for regression analysis; could I compare $R^2$ values between a linear regression, logarithmic regression and exponential smoothing as a method of evaluating the best technique for forecasting?

In-sample fit such as $R^2$ is even more frowned upon as a measure of model quality in forecasting than in other statistical subdisciplines, for all the well-known reasons (if you make your model more and more complex, you will get better and better in-sample fits... but ever worse out-of-sample forecast accuracy). If at all, people will rather use information criteria, such as AIC or BIC.
However, the gold standard in forecast model assessment and model selection is using a hold-out sample. Suppose you have $n$ historical data points. Remove the last $k$ (say, $k=10$, depending on your data and the forecast horizon you actually are interested in). Fit your model (exponential smoothing, whatever) on the first $n-k$ observations, use the fitted model to forecast into the last $k$ observations, and compare this forecast with the hold-out actuals. Do this for a couple of models and choose the one with the best performance on the hold-out. Finally, refit this model using all $n$ observations to create your actual forecast.