A computer-implemented method includes receiving data representing pre-existing instances of an analytics model developed over time; detecting changes in state of the analytics model over time to detect trends; generating a new instance of the analytics model that has been modified based on detected trends in the analytics model; generating new training data based on discovered trends of the analytics model over time; comparing a coverage of the new instance of the analytics model and coverages of the pre-existing instances of the analytics model with the new training data; and determining whether new instance of the analytics model have better coverage than the pre-existing instances of the analytics model with the new training data. A corresponding computer program product and system are also disclosed.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for detecting trends in an analytics model comprising: receiving data representing pre-existing instances of an analytics model developed over time; detecting changes in state of the analytics model over time to detect trends; generating a new instance of the analytics model that has been modified based on detected trends in the analytics model; generating new training data based on discovered trends of the analytics model over time; comparing a coverage of the new instance of the analytics model and coverages of the pre-existing instances of the analytics model with the new training data; and determining whether new instance of the analytics model have better coverage than the pre-existing instances of the analytics model with the new training data. 2. The method of claim 1, wherein the analytics model comprises behavioral data. 3. The method of claim 2, wherein the analytics model is modified so as to reflect changes in the behavioral data. 4. The method of claim 2, wherein the analytics model further comprises an analytic component, the analytic component being associated with metadata, wherein the metadata comprises a description of an analytic technique used by the analytics model, assumptions required for the analytic technique to be valid, constraints on the analytics model, sensitivities of the analytics model, a definition of a type of data on which the analytics model operates, and a definition of an output the analytics model produces. 5. The method of claim 1, wherein the coverage of the new instance of the analytics model is compared with the coverage of at least one other instance of the analytics model using a statistical test. 6. The method of claim 5, wherein the statistical test is an F-test. 7. The method of claim 1, further comprising: identifying one or more training sets, the one or more training being a part of a current model checkpoint object; identifying one or more over-time model trends; and wherein the new training data is generated by using data generator functions to combine the one or more training sets with one or more over-time model trends. 8. A computer system for detecting trends in an analytics model, the system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor to perform: receiving data representing pre-existing instances of an analytics model developed over time; detecting changes in state of the analytics model over time to detect trends; generating a new instance of the analytics model that has been modified based on detected trends in the analytics model; generating new training data based on discovered trends of the analytics model over time; comparing a coverage of the new instance of the analytics model and coverages of the pre-existing instances of the analytics model with the new training data; and determining whether new instance of the analytics model have better coverage than the pre-existing instances of the analytics model with the new training data. 9. The computer system of claim 8, wherein the analytics model comprises behavioral data. 10. The computer system of claim 9, wherein the analytics model is modified so as to reflect changes in the behavioral data. 11. The computer system of claim 9, wherein the analytics model further comprises an analytic component, the analytic component being associated with metadata, wherein the metadata comprises a description of an analytic technique used by the analytics model, assumptions required for the analytic technique to be valid, constraints on the analytics model, sensitivities of the analytics model, a definition of a type of data on which the analytics model operates, and a definition of an output the analytics model produces. 12. The computer system of claim 8, wherein the coverage of the new instance of the analytics model is compared with the coverage of at least one other instance of the analytics model using a statistical test. 13. The computer system of claim 12, wherein the statistical test is an F-test. 14. The computer system of claim 8, further comprising computer program instructions to perform: identifying one or more training sets, the one or more training being a part of a current model checkpoint object; identifying one or more over-time model trends; and wherein the new training data is generated by using data generator functions to combine the one or more training sets with one or more over-time model trends. 15. A computer program product for detecting trends in an analytics model, the computer program product comprising a non-transitory computer readable storage having program instructions embodied therewith, the program instructions executable by a computer, to cause the computer to perform a method comprising: receiving data representing pre-existing instances of an analytics model developed over time; detecting changes in state of the analytics model over time to detect trends; generating a new instance of the analytics model that has been modified based on detected trends in the analytics model; generating new training data based on discovered trends of the analytics model over time; comparing a coverage of the new instance of the analytics model and coverages of the pre-existing instances of the analytics model with the new training data; and determining whether new instance of the analytics model have better coverage than the pre-existing instances of the analytics model with the new training data. 16. The computer program product of claim 15, wherein the analytics model comprises behavioral data. 17. The computer program product of claim 16, wherein the analytics model is modified so as to reflect changes in the behavioral data. 18. The computer program product of claim 16, wherein the analytics model further comprises an analytic component, the analytic component being associated with metadata, wherein the metadata comprises a description of an analytic technique used by the analytics model, assumptions required for the analytic technique to be valid, constraints on the analytics model, sensitivities of the analytics model, a definition of a type of data on which the analytics model operates, and a definition of an output the analytics model produces. 19. The computer program product of claim 15, wherein the coverage of the new instance of the analytics model is compared with the coverage of at least one other instance of the analytics model using a statistical test. 20. The computer program product of claim 19, wherein the statistical test is an F-test.