Synthetic Data Generation for Stress Testing Data Models
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Solution Overview
Problem
Existing data models trained on synthetic datasets may not reliably perform under extreme and anomalous market conditions, as they are not customized to reflect unprecedented scenarios lacking real data samples.
Innovation Solution
A method and system for generating synthetic stress training datasets by creating a stress profile from an analysis of the data model and initial training dataset, modifying the data profile to generate an altered data profile, and using this to create synthetic datasets for stress performance testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If synthetic datasets are used to train data models, then privacy concerns are addressed and data availability is improved, but the models cannot reliably perform under extreme and anomalous market conditions
Solution Approach 1:
The system performs preliminary analysis of the data model to identify weaknesses and vulnerabilities before training. It generates a stress profile that anticipates extreme conditions and prepares synthetic training data accordingly, enabling the model to be pre-trained for scenarios it hasn't encountered in historical data.
Solution Approach 2:
The system modifies data profile parameters by applying stress factors to generate altered data profiles. It changes statistical parameters, data distributions, and feature characteristics to simulate extreme market conditions, thereby training the model on a broader range of scenarios including unprecedented events.
2Measurement precision
If traditional synthetic data generation is used, then baseline market conditions are represented accurately, but extreme and anomalous conditions cannot be simulated
Solution Approach 1:
The system segments the training data generation process into distinct components: baseline data profile generation, stress profile generation, and combined altered data profile creation. This allows separate optimization of baseline accuracy and stress condition simulation, maintaining precision for normal conditions while adding reliability for extreme conditions.
Solution Approach 2:
The system creates composite training datasets by combining baseline synthetic data with stress-modified data. The altered data profile integrates both normal market condition characteristics and extreme condition stressors, producing a composite dataset that trains the model on the full spectrum of possible scenarios.
3Reliability
If real datasets are used for training, then models learn from actual market behavior, but privacy implications and data sensitivity issues arise
Solution Approach 1:
The system creates synthetic copies of real market data that preserve the statistical properties, patterns, and behavioral characteristics of actual market data without containing any real personal or sensitive information. These synthetic replicas enable model training on real-like data while eliminating privacy risks.
Solution Approach 2:
The system transforms real data characteristics into synthetic data by modifying and generalizing parameter distributions. It changes specific data values into statistical patterns that capture market behavior without preserving identifiable information, thereby maintaining training effectiveness while removing privacy concerns.
Data Source
AI summary
The present inventions is directed to systems and methods for automated stress testing of data models and comprises steps of generating, from an initial training dataset used for training a data model, a first data profile comprising a descriptive summary of the initial training dataset, generating a stress profile from an analysis of the data model and the initial training dataset used for training the data model, and modifying the first data profile by the stress profile to generate an altered data profile. A synthetic dataset may then be generated from the altered data profile. A stress performance output of the data model may then be used to identify weak points in the performance of the data model and improve the model's stress performance response using synthetic data.