BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges

ArXiv ID: 2411.06076 “View on arXiv”

Authors: Unknown

Abstract

This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. By leveraging both the capabilities of LLMs and Transformer-based models, this study evaluates BreakGPT and other Transformer-based models for their ability to address the unique challenges posed by highly volatile financial markets. The primary contribution of this work lies in demonstrating the effectiveness of combining time series representation learning with LLM prediction frameworks. We showcase BreakGPT as a promising solution for financial forecasting with minimal training and as a strong competitor for capturing both local and global temporal dependencies.

Keywords: BreakGPT, Large Language Models (LLMs), Transformer-based models, Time Series Forecasting, Asset Price Prediction, General Financial Assets (Time Series)

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats
  • Why: The paper introduces a novel Transformer/LLM architecture (BreakGPT) with specific mathematical components like attention mechanisms, residual connections, and convolutional layers, indicating moderate to high mathematical complexity. However, the empirical evaluation is limited to a single asset (Solana) with a narrow time window and basic metrics (F1-score, accuracy), lacking rigorous backtesting, risk-adjusted performance measures, or robust validation, placing it in the ‘Lab Rats’ quadrant.
  flowchart TD
    A["Research Goal:<br>Predict Sharp Asset Price Surges"] --> B["Methodology:<br>Develop BreakGPT Architecture"]
    B --> C["Data Input:<br>General Financial Assets Time Series"]
    C --> D["Computation:<br>Hybrid Transformer & LLM Processing"]
    D --> E["Computation:<br>Learning Local & Global Dependencies"]
    E --> F["Outcome:<br>Effective Forecasting with Minimal Training"]