Multimodal VQ-VAE Compression with Neural Upsampling for Financial Data
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Solution Overview
Problem
Existing data compression techniques, such as HEVC, result in loss of data during compression, making it challenging to recover important patterns and correlations in financial time-series data and other multimodal data types.
Innovation Solution
A multimodal fintech deep learning core that employs a vector-quantized variational autoencoder (VQ-VAE) system to compress and reconstruct financial data, incorporating multiple data modalities and using advanced fusion techniques to preserve cross-modal correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression techniques are used to reduce data size, then bandwidth efficiency and storage efficiency are improved, but data accuracy and information completeness deteriorate
Solution Approach 1:
The patent segments financial time-series data into multiple correlated channels (e.g., different asset classes, time frequencies, or data types). Each channel is compressed independently using lossy compression, but the correlations between channels are preserved and exploited during reconstruction. This segmentation allows selective preservation of important correlations while tolerating losses in individual channels.
Solution Approach 2:
The patent introduces an intermediary upsampling model that acts as a mediator between the compressed data and the final reconstruction. This model learns the underlying correlations and patterns from multiple compressed channels and generates high-quality reconstructed data that recovers information lost during compression. The intermediary model bridges the gap between compressed representation and accurate reconstruction.
2Adaptability or versatility
If multiple data modalities are processed together, then comprehensive analysis capability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal processing framework that handles multiple data modalities (time-series, text, sentiment, structured data) through a common architecture. The upsampling model is designed to process diverse input types uniformly, applying the same correlation-learning and reconstruction principles across different modalities. This multi-functional approach enables comprehensive analysis without requiring separate specialized systems for each data type.
Solution Approach 2:
The patent merges multiple data modalities into a unified processing pipeline where all data types are fed into a single upsampling model that learns cross-modal correlations. By combining different data sources (financial time-series, news text, sentiment scores, macroeconomic indicators) into one integrated system, the patent captures interactions between modalities that would be missed in separate processing systems, while managing complexity through shared model components.
Data Source
AI summary
A system and methods for processing diverse financial data types using a jointly trained Vector-quantized Variational Autoencoder (VQ-VAE) and neural upsampler. This system efficiently handles time-series, textual, sentiment, and structured tabular data through specialized encoding modules. A novel fusion module integrates these encodings, capturing cross-modal relationships via attention mechanisms and gated fusion units. The fused representation is compressed into a discrete latent space by the VQ-VAE encoder, then reconstructed and enhanced by the VQ-VAE decoder and neural upsampler, respectively. Joint training optimizes all components simultaneously, using a comprehensive loss function that balances reconstruction quality across modalities with upsampling performance. This approach enables superior data compression, reconstruction, and analysis, leveraging inter-modal correlations to improve financial forecasting, risk assessment, and decision-making. The system's ability to explore the latent space facilitates generation of new, synthetic financial scenarios for robust model testing and strategy development.