Hybrid Variational Neural Network for Reactive Metric Prediction
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Solution Overview
Problem
Existing machine learning models struggle to quickly and accurately react to macroeconomic changes, leading to delays and inaccuracies in predicting metrics such as default rates and consumer behavior during times of economic uncertainty.
Innovation Solution
A deep learning model combining a linear neural network and a variational auto-encoder is used to generate a concatenated output vector, enabling timely and accurate prediction of metrics by jointly training both components to minimize offsets and improve reactivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional machine learning model is used to predict metrics, then the model structure is simple and easy to implement, but the model cannot quickly and accurately react to macroeconomic changes, leading to prediction delays and inaccuracies
Solution Approach 1:
The patent combines a linear neural network and a variational auto-encoder into a hybrid model architecture. The linear neural network captures linear relationships in the data, while the variational auto-encoder learns non-linear patterns and latent representations. By merging these two different modeling approaches, the system achieves both fast adaptation to macroeconomic changes and high prediction accuracy, resolving the contradiction between reactivity and precision.
Solution Approach 2:
The model uses a composite architecture combining different neural network components (linear neural network layers and variational auto-encoder layers) with distinct functional properties. This composite structure allows the system to leverage the speed and interpretability of linear models while incorporating the adaptive learning capabilities of non-linear auto-encoders, achieving both rapid response and accurate predictions.
2Measurement precision
If a complex deep learning model is used to improve prediction accuracy, then the prediction precision improves, but the model complexity increases and computational requirements increase
Solution Approach 1:
The patent segments the deep learning model into distinct functional modules: a linear neural network component for capturing linear relationships and a variational auto-encoder component for learning non-linear latent representations. This segmentation allows each component to be optimized independently and contributes to the overall accuracy without requiring an monolithic complex architecture, making the system more manageable and computationally efficient.
Solution Approach 2:
The variational auto-encoder component introduces dynamic latent representations that adapt to changing macroeconomic conditions. The probabilistic nature of the VAE allows the model to dynamically adjust its internal representations based on input data distributions, improving prediction accuracy in response to macroeconomic changes without requiring excessive model complexity.
3Speed
If the model is trained to be highly reactive to external changes, then the response time improves, but the prediction accuracy may decrease due to overfitting to recent patterns
Solution Approach 1:
The patent incorporates a feedback mechanism where the model's predictions are continuously evaluated against actual outcomes. The loss function combines reconstruction error from the auto-encoder with prediction error terms, providing feedback that guides the model to balance reactivity with accuracy. This feedback loop prevents overfitting to recent patterns while maintaining rapid response capability.
Solution Approach 2:
The model uses parameter sharing and regularization techniques to control the balance between reactivity and accuracy. By carefully tuning hyperparameters such as the weighting between reconstruction loss and prediction loss, and applying regularization to prevent overfitting, the model achieves fast response times without sacrificing prediction accuracy.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for generating an expected data metric. A set of features is provided to a machine learning model. The machine learning module includes a linear neural network and a variational auto-encoder. The linear neural network uses the set of features to generate a first vector. The variational auto-encoder generates a second vector using the set of features. The expected data metric is determined based on an output vector. The output vector is obtained using the first vector and the second vector.