The simple average is commonly used to combine the predictions out of different predictive models. Apart form the simple average, what are the other methods that can be used for combining the predictive models to get more accurate predictions?
Provided you have raw data you could use in this process, one could use the various different models and treat them as basis functions of sorts that you wish to merge together in a least square sense.
You could then merge the various models using a least square fit based on whatever data you have at your disposal. This is certainly different than simple averaging.