System, method and computer-accessible medium for making a prediction from market data
Inventors
KAILAS, GEORGE • HANNLEY, SEAN • Mishra, Bhubaneswar
Assignees
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Publication Number
US-12694297-B2
Publication Date
2026-07-28
Expiration Date
Abstract
An exemplary system, method and computer-accessible medium for generating a model(s), can include, for example, receiving first information related to raw data, generating second information by formatting the first information, generating third information related to a feature set(s) of the second information, generating the model(s) based on the second and third information. Fourth information related to a user-defined regularization of the second information can be received, fifth information can be generated based on a reformatting of the second information using the fourth information. A prediction(s) can be generated based on the model(s). The prediction(s) can be generated based on a time horizon(s).
Core Innovation
Not explicitly described in patent.
Not explicitly described in patent.
Claims Coverage
The provided excerpt contains three independent claims (clm-00001, clm-00021, and clm-00022). Each claim includes the same core set of inventive elements: receiving first information related to raw data, generating second information by formatting the first information into a plurality of data columns, determining a potential informational value and covariance, generating a feature set with metadata, and generating at least one model using an islanding procedure with islands formed without regard to a class variable and species-to-island assignment based on performance, followed by evolution for a fraction of a number of generations used on a full data set.
Information formatting with potential informational value and covariance
Receiving first information related to raw data; generating second information by formatting the first information, the second information comprising a plurality of data columns; determining a potential informational value of each of the plurality of data columns; determining a covariance between each of the plurality of data columns; and generating third information comprising a feature set based on the determined potential informational value of each of the plurality of data columns and the covariance between each of the plurality of data columns.
Feature set metadata generation for model training
Generating fourth information comprising metadata related to the second information.
Islanding procedure with subset clusters without regard to class variable
Generating at least one model using an islanding procedure based on the second and third information, wherein the islanding procedure includes generating, using the computer arrangement or computer hardware arrangement, a plurality of islands comprising subset clusters of the second information without regard to a class variable.
Performance-based species-island pairing and fractional evolution
Generating a plurality of species comprising groups of evolutionary neural networks using the computer arrangement or computer hardware arrangement; evaluating the performance of the plurality of species on each of the plurality of generated islands; assigning a fraction of the plurality of species to each of the plurality of islands to be species-island pairs based on the performance of the species on each island; and continuing an evolution of each of the species-island pairs for a fraction of a number of generations used on a full data set.
Across the independent claims, the inventive approach centers on constructing a feature set from potential informational value and covariance computed from formatted data columns, associating metadata, and then generating models via an islanding procedure that clusters subsets without regard to a class variable, evolves species of evolutionary neural networks on each island, pairs species to islands based on performance, and continues evolution only for a fraction of generations used on a full data set.
Stated Advantages
Documented Applications
No documented applications found
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