Embodiments for ensemble policy generation for prediction systems by a processor. Policies are generated and/or derived for a set of ensemble models to predict a plurality of target variables for streaming data such that the plurality of policies enables dynamic adjustment of the prediction system. One or more of the policies are updated according to one or more error states of the set of ensemble models.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, by a processor, for ensemble policy generation for prediction systems, comprising: deriving a plurality of policies for a set of ensemble models to predict a plurality of target variables for streaming data such that the plurality of policies enables dynamic adjustment of the prediction system; and dynamically updating one or more of the plurality of policies according to one or more error states of the set of ensemble models. 2. The method of claim 1, wherein the generating further includes generating the plurality of policies according to contextual information, model similarities, ensemble model definitions, prediction quality metrics, ground truth data, or a combination thereof. 3. The method of claim 1, further including utilizing the error states for a partial set of the set of ensemble models to dynamically adjust the plurality of policies for a remaining set of the set of ensemble models. 4. The method of claim 1, further including identifying model similarity for the set of ensemble models according to prediction accuracy of a plurality of conditions. 5. The method of claim 1, further including identifying one or more ensemble models from the set of ensemble models having model similarity based one or more parameters and a plurality of conditions. 6. The method of claim 1, further including dynamically adjusting the one or more of the plurality of policies for a first ensemble model according to a performance of a second ensemble model having model similarity features. 7. The method of claim 1, further including mapping sampled predictive qualities between those of the set of ensemble models having model similarity features. 8. A system for ensemble policy generation for prediction systems, comprising: one or more computers with executable instructions that when executed cause the system to: derive a plurality of policies for a set of ensemble models to predict a plurality of target variables for streaming data such that the plurality of policies enables dynamic adjustment of the prediction system; and dynamically update one or more of the plurality of policies according to one or more error states of the set of ensemble models. 9. The system of claim 8, wherein the executable instructions further generate the plurality of policies according to contextual information, model similarities, ensemble model definitions, prediction quality metrics, ground truth data, or a combination thereof. 10. The system of claim 8, wherein the executable instructions further dynamically adjust one or more parameters of an ensemble model using one of the plurality of policies according to a performance of similar ensemble model. 11. The system of claim 8, wherein the executable instructions utilize the error states for a partial set of the set of ensemble models to dynamically adjust the plurality of policies for a remaining set of the set of ensemble models. 12. The system of claim 8, wherein the executable instructions identify model similarity for the set of ensemble models according to prediction accuracy of a plurality of conditions. 13. The system of claim 8, wherein the executable instructions dynamically adjust the one or more of the plurality of policies for a first ensemble model according to a performance of a second ensemble model having model similarity features. 14. The system of claim 8, wherein the executable instructions sampled predictive qualities between those of the set of ensemble models having model similarity features. 15. A computer program product for, by a processor, ensemble policy generation for prediction systems, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising: an executable portion that derives a plurality of policies for a set of ensemble models to predict a plurality of target variables for streaming data such that the plurality of policies enables dynamic adjustment of the prediction system; and an executable portion that dynamically updates one or more of the plurality of policies according to one or more error states of the set of ensemble models. 16. The computer program product of claim 15, further including an executable portion that generates the plurality of policies according to contextual information, model similarities, ensemble model definitions, prediction quality metrics, ground truth data, or a combination thereof. 17. The computer program product of claim 15, further including an executable portion that dynamically adjusts one or more parameters of an ensemble model using one of the plurality of policies according to a performance of similar ensemble model. 18. The computer program product of claim 15, further including an executable portion that utilizes the error states for a partial set of the set of ensemble models to dynamically adjust the plurality of policies for a remaining set of the set of ensemble models. 19. The computer program product of claim 15, further including an executable portion that identifies model similarity for the set of ensemble models according to prediction accuracy of a plurality of conditions. 20. The computer program product of claim 15, further including an executable portion that: dynamically adjusts the one or more of the plurality of policies for a first ensemble model according to a performance of a second ensemble model having model similarity features; or maps sampled predictive qualities between those of the set of ensemble models having model similarity features.