Flow Data Prediction for Financial Anomaly Detection
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Solution Overview
Problem
Existing methods for predicting financial anomalies, particularly medium intensity crashes, face challenges due to class imbalance issues, where abnormalities are scarce compared to regular market activity, hindering the effectiveness of classification techniques.
Innovation Solution
A method and system utilizing a recurrent neural network configured as a cost-sensitive machine learning tool, applying flow feature data to detect abnormalities in financial markets by generating flow state predictions and updating system parameters, incorporating a Kalman filter and echo state network to address class imbalance and improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classification techniques are used to detect financial anomalies, then detection capability is provided, but accuracy deteriorates due to class imbalance where abnormalities are scarce compared to regular market activity
Solution Approach 1:
The system performs preliminary feature extraction to generate flow features from market data before classification. This preprocessing step transforms raw market data into meaningful flow characteristics that better represent the underlying market dynamics, enabling more accurate anomaly detection despite class imbalance
Solution Approach 2:
The system changes the parameter representation by transitioning from traditional market data parameters to flow-based parameters (density, velocity, pressure). This parameter transformation captures the macroscopic behavior of market participants more effectively, improving the classifier's ability to distinguish anomalies from normal activity
2Ease of manufacture
If traditional machine learning models are applied to financial anomaly detection, then implementation is straightforward, but detection accuracy for medium intensity crashes remains insufficient
Solution Approach 1:
The system replaces traditional statistical and machine learning models with a physics-based flow model. By substituting mechanical/mathematical approaches with physics-based reasoning (treating market participants as fluid particles), the system achieves superior detection accuracy for medium intensity crashes while maintaining interpretability
3Quantity of substance
If focus is placed on major crisis forecasting, then significant research attention is given, but medium intensity crashes remain underinvestigated and undetected
Solution Approach 1:
The system applies local quality by tailoring the flow model parameters and features specifically for detecting medium intensity crashes, rather than using a generic model for all market anomalies. The flow features (density, velocity, pressure) are optimized to capture the subtle characteristics of moderate market stress events
Solution Approach 2:
The system performs preliminary analysis using flow features to identify conditions precedent to medium intensity crashes. By monitoring flow characteristics before crashes occur, the system can detect emerging stress conditions that traditional models miss, enabling early warning of moderate market events
Data Source
AI summary
A method for detecting an abnormality in a flow system includes obtaining, with a processor, input flow data for the flow system over a series of time intervals, sequentially processing, with the processor, the input flow data to generate, for each time interval in the series of time intervals, flow feature data, the flow feature data being representative of a plurality of flow parameters for the input flow data at the time interval in the series of time intervals, applying, with the processor, the flow feature data to a machine learning tool, the machine learning tool being configured to provide an assessment, for each time interval in the series of time intervals, of whether the input flow data is indicative of the abnormality being present in a following time interval in the series of time intervals, and providing, with the processor, output data indicative of the assessment.