Apparatus and methods for high-order system growth modeling
An apparatus and method for high-order modeling of a system is disclosed. The apparatus comprises at least a computing device to receive a plurality of system data, determine at least a current rate of growth for a plurality of categories of system data, generate at least a second-order model of the projected growth, identify at least a decrease in the second-order model for projected growth, identify a pattern associating the decrease in the second-order model to at least a single system datum, and configure a remote display to export a summary of the identified decrease in second-order model, identified pattern, and the recommended user actions to optimize the system growth rate. The method comprises a machine-learning model to execute the process described above.
1. An apparatus for high-order system growth modeling, wherein the apparatus comprises: a computing device configured to: receive a plurality of system data describing a system; determine at least a current rate of growth according to at least one category of system data; generate at least a second-order model of projected growth, wherein each second-order model of the at least a second-order model corresponds to a current rate of growth of the at least a current rate of growth; receive training data, wherein the training data comprises a plurality of data entries containing a plurality of environmental factor data correlated to a plurality of user summary data; sanitize the training data using a dedicated hardware unit comprising circuitry configured to perform signal processing operations, wherein sanitizing the training data comprises: determining by the dedicated hardware unit that a training data entry has a signal to noise ratio below a threshold value indicating poor data quality; and removing the training data entry from the training data; train a machine-learning model using the sanitized training data; identify at least a decrease and at least a pattern of data, wherein: the at least a decrease includes, for each category of the at least one category of system data, a decrease in a second-order model, of the at least a second-order model, that is associated with the category, wherein identifying the decrease comprises: simulating, by the trained machine-learning model, the at least a current rate of growth by applying a comprehensive set of hypothetical inputs representative of potential actual environmental factors; comparing, by the trained machine-learning model, simulated outputs prior to any change in inputs with the simulated outputs subsequent to any change in inputs; generating, by the trained machine-learning model, a summary for a user; and the at least a pattern of data includes, for each category of the at least one category of system data, a pattern of data causally associated with the decrease in an associated second-order model of the plurality of second-order models; and configure a remote device to generate a display as a function of the decrease in each second-order model of the at least a second-order model and each pattern of data; wherein the machine-learning model further aggregates individual comparisons to associate inputs with positive or negative economic implications based on user feedback of actual environmental response; and the machine-learning model further recommends or deters the user from certain proposed actions based on learned associations.
2. The apparatus of claim 1 , wherein receiving the plurality of system data comprises receiving the plurality of system data from a graphical user interface.
3. The apparatus of claim 1 , wherein determining the at least a current rate of growth comprises determining the at least a current rate of growth for a plurality of user-defined categories.
4. The apparatus of claim 1 , wherein generating the at least a second-order model of projected growth comprises determining a rate of change of the at least a current rate of growth with respect to economic implications.
5. The apparatus of claim 1 , wherein identifying the at least a decrease in the at least a second-order model comprises: performing a real-time continual analysis on the at least a second-order model of projected growth; and identifying the at least a decrease in the at least a second-order model as a function of the real-time continual analysis.
6. The apparatus of claim 1 , wherein identifying the at least a pattern of data comprises: quantifying a plurality of actual environmental factors, wherein each actual environmental factor of the plurality of actual environmental factors is associated to the at least a decrease in the at least a second order model; and identifying at least a dissociation protocol configured to dissociate the plurality of actual environmental factors from the at least a decrease in the at least a second order model.
7. The apparatus of claim 3 , wherein each defined category of system data defined and included within the at least a current rate of growth will be amplified or reduced using a prioritization coefficient, wherein said prioritization comprises: identifying a relative importance between each defined category of system data; assigning a score as a function of the relative importance of each defined category of system data; incorporating the assigned prioritization score into the current rate of growth, wherein said incorporation would be accomplished by the user or a machine-learning model.
8. The apparatus of claim 1 , wherein the at least a second-order model of projected growth comprises a third-order model of projected growth comprising a projected rate of growth of the current rate of growth.
9. The apparatus of claim 8 , wherein the computing device is further configured to update the at least a second order model as a function of the third-order model of projected growth.
10. A method for high-order system growth modeling, wherein the method comprises: receiving, by a computing device, a plurality of system data describing a system; determining, by the computing device, at least a current rate of growth according to at least one category of the plurality of system data; generate, by the computing device, at least a second-order model of projected growth, wherein each second-order model of the at least a second-order model corresponds to a current rate of growth of the at least a current rate of growth; receiving, by the computing device, training data, wherein the training data comprises a plurality of data entries containing a plurality of environmental factor data correlated to a plurality of user summary data; sanitizing, by the computing device, the training data using a dedicated hardware unit comprising circuitry configured to perform signal processing operations, wherein sanitizing the training data comprises: determining by the dedicated hardware unit that a training data entry has a signal to noise ratio below a threshold value indicating poor data quality; and removing the training data entry from the training data; training, by the computing device, a machine-learning model using the sanitized training data; identifying, by the computing device, at least a decrease and at least a pattern of data, wherein: the at least a decrease includes, for each category of the at least one category of system data, a decrease in a second-order model, of the at least a second-order model, that is associated with the category, wherein identifying the decrease comprises: simulating, by the trained machine-learning model, the at least a current rate of growth by applying a comprehensive set of hypothetical inputs representative of potential actual environmental factors; comparing, by the trained machine-learning model, simulated outputs prior to any change in inputs with the simulated outputs subsequent to any change in inputs; generating, by the trained machine-learning model, a summary for a user; and the at least a pattern of data includes, for each category of the at least one category of system data, a pattern of data causally associated with the decrease in an associated second-order model of the plurality of second-order models; and configuring, by the computing device, a remote device to generate a display as a function of the decrease in each second-order model of the at least a second-order model and each pattern of data; wherein the machine-learning model is further configured to perform: an aggregation of individual comparisons to associate inputs with positive or negative economic implications; and a recommendation to either encourage or deter the user from certain proposed actions based on learned associations.
11. The method of claim 10 , wherein receiving the plurality of system data by the computing device comprises receiving the plurality of system data from a graphical user interface.
12. The method of claim 10 , wherein the determination of the at least a current rate of growth comprises determining, by the computing device, the at least a current rate of growth for a plurality of user-defined categories.
13. The method of claim 10 , wherein the generation of the at least a second-order model of projected growth, by the computing device, comprising; the determination of a rate of change of the at least a current rate of growth with respect to economic implications.
14. The method of claim 10 , wherein identification of the at least a decrease in the at least a second-order model comprises: performance, by the computing device, a real-time continual analysis on the at least a second-order model of projected growth; and identification, by the computing device, of the at least a decrease in the at least a second-order model as a function of the real-time continual analysis.
15. The method of claim 10 , wherein identification, by the computing device, of the at least a pattern of data comprises: quantification, using the computing device, of a plurality of actual environmental factors, wherein each actual environmental factor of the plurality of actual environmental factors is associated to the at least a decrease in the at least a second-order model; and identification, using the computing device, of at least a dissociation protocol configured to dissociate the plurality of actual environmental factors from the at least a decrease in the at least a second-order model.
16. The method of claim 12 , wherein each defined category of system data within the at least a current rate of growth will be amplified or reduced using a prioritization coefficient by the computing device, comprising: identification, using the computing device, of a relative importance between each defined category of system data; assignment, using the computing device, of a score as a function of the relative importance of each defined category of system data such that a category that is significantly more important than a separately defined category of system data, wherein a former score would be a 10 with a latter score a 1; and incorporation, using the computing device, of the assigned prioritization score into the current rate of growth.
17. The method of claim 10 , wherein the generation, using the computing device, of at least a second-order model of projected growth comprises generating a third-order model of projected growth.
18. The method of claim 17 , wherein the method further comprises updating the at least a second-order model as a function of the third-order model of projected growth.