Monte Carlo Simulation Filtering for Neural Network Prediction
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Solution Overview
Problem
Current methods for testing the accuracy of prototype indices, such as backtesting, lack the ability to properly evaluate the predictive ability of a prototype index going forward, as they primarily rely on retrodiction and cross-validation with previous time periods, missing forward testing capabilities.
Innovation Solution
A computer-implemented method and system that performs Monte Carlo simulations based on historical index values to predict future values, calculates attributes, correlates these simulations with historical data to identify a filtered subset of accurate predictions, and feeds them into a deep neural network (DNN) to output confidence scores for each prediction, enabling forward testing of predictive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If backtesting methods are used to test prototype indices, then historical data can be utilized, but the predictive ability for future values cannot be properly evaluated
Solution Approach 1:
The system performs Monte Carlo simulations to generate multiple possible future index values before actual prediction occurs. By pre-simulating various scenarios based on historical data and attributes, the system prepares predictive models in advance that can be evaluated for their forward-looking accuracy, resolving the contradiction between using historical data and evaluating future predictive ability.
Solution Approach 2:
The system creates synthetic future index values through Monte Carlo simulations that copy and extend historical patterns. These simulated future values are then fed into the DNN for evaluation, allowing the system to assess predictive ability without requiring actual future data to exist, thus enabling forward testing capability.
2Adaptability or versatility
If Monte Carlo simulations are performed to predict future values, then forward testing capability is achieved, but computational complexity increases
Solution Approach 1:
The system segments the predictive modeling process into distinct stages: (1) calculating attributes from historical data, (2) performing Monte Carlo simulations to generate future values, (3) filtering simulations based on accuracy thresholds, and (4) feeding filtered results into the DNN. This segmentation allows computational complexity to be managed systematically, with each stage processing specific portions of the overall computational task.
Solution Approach 2:
The system introduces an intermediary filtering step that correlates Monte Carlo simulations with calculated attributes to identify a filtered subset of predictions above a defined accuracy threshold. This intermediary process reduces the volume of data entering the DNN, thereby managing computational complexity while maintaining forward testing capability.
3Measurement precision
If a deep neural network is used to evaluate predictions, then prediction accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary filtering of Monte Carlo simulations by correlating them with historical attributes and applying accuracy thresholds before the data enters the DNN. This preliminary action reduces the amount of data the neural network must process, thereby decreasing processing time while maintaining the ability to achieve high prediction accuracy through the DNN's sophisticated analysis of the filtered subset.
Data Source
AI summary
Testing prototype indices is provided. The method comprises receiving input of a number of historical index values and performing a number of Monte Carlo simulations based on the historical index values to predict future index values. A number of attributes of the historical index values are calculated, and the Monte Carlo simulations are correlated with the attributes to identify a filtered subset of predicted future index values having an accuracy above a defined threshold. The filtered subset of predicted future index values are fed into a deep neural network (DNN) that outputs a confidence score for each of the filtered subset of predictive future index values.