VQ-VAE Neural Upsampling for Compressed Financial Time-Series
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Solution Overview
Problem
Current data compression techniques, such as HEVC, result in loss of data during compression and decompression, particularly in financial time-series data, which can lead to inaccurate representations and challenges in data management and analysis.
Innovation Solution
A system utilizing a jointly trained Vector Quantized Variational Autoencoder (VQ-VAE) and neural upsampler is introduced, which compresses data into a discrete latent space, reconstructs the data, and enhances it to recover information lost during compression, achieving efficient compression and high-quality reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression techniques are used to reduce data size, then storage and transmission efficiency is improved, but data accuracy and information completeness deteriorates
Solution Approach 1:
The patent introduces a neural upsampler as an intermediary component between the decompressor and the final data output. This neural network model acts as a mediator that takes compressed financial time-series data as input and generates reconstructed high-resolution output by learning the mapping from compressed to original data space, thereby recovering information lost during lossy compression while maintaining storage efficiency
Solution Approach 2:
The patent replaces traditional mechanical or algorithmic upsampling methods (such as linear interpolation or nearest-neighbor resampling) with a neural network-based approach. The neural upsampler learns complex non-linear relationships in the data during training and applies these learned patterns to reconstruct high-quality financial time-series data from compressed inputs, achieving superior reconstruction accuracy compared to conventional methods
2Measurement precision
If traditional upsampling methods are used after decompression, then data resolution is improved, but correlation information between multiple time series is lost
Solution Approach 1:
The patent merges multiple compressed time-series inputs into a single unified neural network processing stream. The neural upsampler simultaneously processes all input time series and their inter-correlations through shared neural network weights and operations, preserving the statistical relationships and dependencies between different financial instruments while reconstructing high-resolution data for all series
Solution Approach 2:
The neural upsampler is designed as a universal model that can handle multiple different time-series inputs simultaneously. The same network architecture and weights are applied across all input series, enabling the model to generalize correlation patterns learned from training data and apply them to reconstruct various financial instruments' data while maintaining their interrelationships
Data Source
AI summary
A system and methods for upsampling compressed data using a jointly trained Vector Quantized Variational Autoencoder (VQ-VAE) and neural upsampler. The system compresses input data into a discrete latent space using a VQ-VAE encoder, reconstructs the data using a VQ-VAE decoder, and enhances the reconstructed data using a neural upsampler. The VQ-VAE and neural upsampler are jointly trained using a combined loss function, enabling end-to-end optimization. The system allows for efficient compression and high-quality reconstruction of various data types, including financial time-series, images, audio, video, sensor data, and text. The learned discrete latent space can be explored and manipulated using techniques such as interpolation, extrapolation, and vector arithmetic to generate new or modified data samples. The system finds applications in data storage, transmission, analysis, and generation across multiple domains.