US 2025/0363333 A1 · REAL-TIME TIME SERIES FORECASTING USING A COMPOUND LARGE CODEWORD MODEL

Abstract

A system and method for real-time financial data analysis and market prediction. The system processes diverse inputs, including financial news snippets and trading data, through adaptive codebook generation and codeword allocation. A projection network fuses different data types, creating unified representations for a latent transformer core. The system's architecture enables efficient handling of multi-modal financial data, capturing complex relationships between news sentiment and market behavior. An adaptive codebook generation method ensures the system remains responsive to evolving market conditions. This approach aims to provide more accurate and timely market predictions by leveraging both textual and numerical financial data in a sophisticated, integrated manner.

Claims12

Dependent claims

A deep learning system for real-time time series forecasting using a compound large codeword model, comprising one or more computers with executable instructions that, when executed, cause the deep learning system to:

receive a variety of data inputs which includes a plurality of data types;
allocate codewords to each data input, wherein codewords are mapped to a corresponding codebook;
fuse codewords of dissimilar data types together into a single codeword representation;
process the single codeword representation through a machine learning core;
generate an output based on a plurality of single codeword representations.

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Real-time time series forecasting using a compound large codeword model

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Real-time time series forecasting using a compound large codeword model

Life of this patent

Abstract

Claims12