There is a host of metrics for evaluating predictive performance. They are all based on aggregating the forecast errors in some form. The two most famous metrics are RMSE (Root-mean-squared-error) and MAPE (Mean-Absolute-Percentage-Error). In an earlier posting (Feb-23-2006) I disclosed a secret deciphering method for computing these metrics.
Although these two have been the most popular in software, competitions, and published papers, they have their shortages. One serious flaw of the MAPE is that zero counts contribute to the MAPE the value of infinity (because of the division by zero). One solution is to leave the zero counts out of the computation, but then these counts and their predictive error must be reported separately.
I found a very good survey paper of various metrics, which lists the many different metrics and their advantages and weaknesses. The paper, Another look at measures of forecast accuracy,(International Journal of Forecasting, 2006), by Hindman and Koehler, concludes that the best metric to use is the Mean Absolute Scaled Error, which has the mean acronym MASE.