BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges
This work addresses financial forecasting for investors or analysts, but it appears incremental as it combines existing LLM and Transformer methods for a specific domain.
The paper tackles the problem of predicting sharp upward movements in asset prices in volatile financial markets by introducing BreakGPT, a novel LLM architecture adapted for time series forecasting, and demonstrates its effectiveness as a promising solution with minimal training.
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.