A method, system, and computer program product for selecting forecasting model complexity using eigenvalues are provided in the illustrative embodiments A process is represented in a model. The model comprises a mathematical representation of the process in a certain degree. A first portion of historical data generated by the process during a first period is selected and includes an actual value of an outcome of the process and a value of a feature influencing the process during the first period. A prediction is made of a predicted value of the outcome. A difference between the prediction and the actual value of the outcome is determined. The difference is represented as a change in a distribution of eigenvalues. According to the change, a second model is to represent the process. The second model comprises a second mathematical representation of the process in a different degree.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for selecting forecasting model complexity using eigenvalues, the method comprising: representing a process in a model, wherein the model comprises a mathematical representation of the process in a certain degree, and wherein instructions encoding the mathematical representation execute using a processor and a memory in a data processing system; selecting a first portion of historical data generated by the process during a first period, wherein the first portion comprises an actual value of an outcome of the process, and a value of a feature influencing the process during the first period; making a prediction, wherein the prediction comprises a predicted value of the outcome; determining a difference between the prediction and the actual value of the outcome; representing the difference as a change in a distribution of eigenvalues; and selecting, according to the change in the distribution of the eigenvalues, a second model to represent the process, wherein the second model comprises a second mathematical representation of the process in a different degree. 2. The method of claim 1, further comprising: adjusting a set of parameters corresponding to the second model to form an adjusted second model, wherein the adjusted second model produces a second prediction, wherein the second prediction comprises a second predicted value of the outcome; and accepting the adjusted second model as representative of the process, responsive to determining that the second prediction is within a tolerance limit of the actual value of the outcome. 3. The method of claim 2, wherein the adjusting makes adjustments to the set of parameters, further comprising: comparing the adjustments to an instance of previous adjustments to the set parameters; and reporting, responsive to a difference between the adjustments and the previous adjustments exceeding a threshold, an anomaly in a data-generation process. 4. The method of claim 1, further comprising: determining a level of fit between the model and the process, the level of the fit corresponding to the difference between the prediction and the actual value of the outcome; and selecting a third model from a plurality of models, wherein each model in the plurality of models corresponds to a different level of fit. 5. The method of claim 1, further comprising: representing the model as a matrix of values. 6. The method of claim 1, further comprising: representing the feature as a vector. 7. The method of claim 1, further comprising: comparing the eigenvalues to at least one threshold, wherein the at least one threshold defines a plurality of ranges of eigenvalues, wherein the comparing identifies a range from the plurality of ranges, and wherein the identified range includes an eigenvalues from the eigenvalues; selecting the second model from a plurality of models, wherein a model in the plurality of models corresponds to a range in the plurality of ranges. 8. The method of claim 7, wherein each model in the plurality of models represents the process with a mathematical representation in a different degree, wherein a degree in a particular mathematical representation is indicative of a complexity of the particular mathematical representation. 9. The method of claim 1, wherein the method is embodied in a computer program product comprising one or more computer-readable tangible storage devices and computer-readable program instructions which are stored on the one or more computer-readable tangible storage devices and executed by one or more processors. 10. The method of claim 1, wherein the method is embodied in a computer system comprising one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices and program instructions which are stored on the one or more computer-readable tangible storage devices for execution by the one or more processors via the one or more memories and executed by the one or more processors. 11. A computer program product for selecting forecasting model complexity using eigenvalues, the computer program product comprising: one or more computer-readable tangible storage devices; program instructions, stored on at least one of the one or more storage devices, to represent a process in a model, wherein the model comprises a mathematical representation of the process in a certain degree, and wherein instructions encoding the mathematical representation execute using a processor and a memory in a data processing system; program instructions, stored on at least one of the one or more storage devices, to select a first portion of historical data generated by the process during a first period, wherein the first portion comprises an actual value of an outcome of the process, and a value of a feature influencing the process during the first period; program instructions, stored on at least one of the one or more storage devices, to make a prediction, wherein the prediction comprises a predicted value of the outcome; program instructions, stored on at least one of the one or more storage devices, to determine a difference between the prediction and the actual value of the outcome; program instructions, stored on at least one of the one or more storage devices, to represent the difference as a change in a distribution of eigenvalues; and program instructions, stored on at least one of the one or more storage devices, to select, according to the change in the distribution of the eigenvalues, a second model to represent the process, wherein the second model comprises a second mathematical representation of the process in a different degree. 12. The computer program product of claim 11, further comprising: program instructions, stored on at least one of the one or more storage devices, to adjust a set of parameters corresponding to the second model to form an adjusted second model, wherein the adjusted second model produces a second prediction, wherein the second prediction comprises a second predicted value of the outcome; and program instructions, stored on at least one of the one or more storage devices, to accept the adjusted second model as representative of the process, responsive to determining that the second prediction is within a tolerance limit of the actual value of the outcome. 13. The computer program product of claim 12, wherein the program instructions to adjust makes adjustments to the set of parameters, further comprising: program instructions, stored on at least one of the one or more storage devices, to compare the adjustments to an instance of previous adjustments to the set parameters; and program instructions, stored on at least one of the one or more storage devices, to report, responsive to a difference between the adjustments and the previous adjustments exceeding a threshold, an anomaly in a data-generation process. 14. The computer program product of claim 11, further comprising: program instructions, stored on at least one of the one or more storage devices, to determine a level of fit between the model and the process, the level of the fit corresponding to the difference between the prediction and the actual value of the outcome; and program instructions, stored on at least one of the one or more storage devices, to select a third model from a plurality of models, wherein each model in the plurality of models corresponds to a different level of fit. 15. The computer program product of claim 11, further comprising: program instructions, stored on at least one of the one or more storage devices, to represent the model as a matrix of values. 16. The computer program product of claim 11, further comprising: program instructions, stored on at least one of the one or more storage devices, to represent the feature as a vector. 17. The computer program product of claim 11, further comprising: program instructions, stored on at least one of the one or more storage devices, to compare the eigenvalues to at least one threshold, wherein the at least one threshold defines a plurality of ranges of eigenvalues, wherein the comparing identifies a range from the plurality of ranges, and wherein the identified range includes an eigenvalues from the eigenvalues; program instructions, stored on at least one of the one or more storage devices, to select the second model from a plurality of models, wherein a model in the plurality of models corresponds to a range in the plurality of ranges. 18. The computer program product of claim 17, wherein each model in the plurality of models represents the process with a mathematical representation in a different degree, wherein a degree in a particular mathematical representation is indicative of a complexity of the particular mathematical representation. 19. A computer system for selecting forecasting model complexity using eigenvalues, the computer system comprising: one or more processors, one or more computer-readable memories and one or more computer-readable tangible storage devices; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to represent a process in a model, wherein the model comprises a mathematical representation of the process in a certain degree, and wherein instructions encoding the mathematical representation execute using a processor and a memory in a data processing system; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to select a first portion of historical data generated by the process during a first period, wherein the first portion comprises an actual value of an outcome of the process, and a value of a feature influencing the process during the first period; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to make a prediction, wherein the prediction comprises a predicted value of the outcome; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to determine a difference between the prediction and the actual value of the outcome; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to represent the difference as a change in a distribution of eigenvalues; and program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to select, according to the change in the distribution of the eigenvalues, a second model to represent the process, wherein the second model comprises a second mathematical representation of the process in a different degree. 20. The computer system of claim 19, further comprising: program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to adjust a set of parameters corresponding to the second model to form an adjusted second model, wherein the adjusted second model produces a second prediction, wherein the second prediction comprises a second predicted value of the outcome; and program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to accept the adjusted second model as representative of the process, responsive to determining that the second prediction is within a tolerance limit of the actual value of the outcome.