14. The method of claim 12 , the method further comprises: generating a closed-world submodel based upon the observed data, the closed-world submodel being used to generate the probabilities of previously observed events recurring; generating an open-world submodel by evaluating a likelihood of observing the non-observed data over a future period of time, the open-world submodel being used to generate the probabilities of previously non-observed events occurring; and combining outputs of the closed-world submodel and the open-world submodel based upon a weight to yield the prediction.
14. The method of claim 12 , the method further comprises: generating a closed-world submodel based upon the observed data, the closed-world submodel being used to generate the probabilities of previously observed events recurring; generating an open-world submodel by evaluating a likelihood of observing the non-observed data over a future period of time, the open-world submodel being used to generate the probabilities of previously non-observed events occurring; and combining outputs of the closed-world submodel and the open-world submodel based upon a weight to yield the prediction. 16. The method of claim 14 , combining the closed-world submodel and the open-world submodel further comprising: determining characteristics of a user; identifying a subgroup to which the user belongs based upon the characteristics; forecasting a trajectory of a submodel weight that varies as a function of observation time based upon the identified subgroup; and combining the closed-world submodel and the open-world submodel by utilizing the forecasted submodel weight.