BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges

TL;DR

BreakGPT is a new large language model architecture designed for predicting sudden upward surges in asset prices, demonstrating effectiveness in volatile financial markets by combining time series learning with LLM capabilities.

Contribution

This work introduces BreakGPT, a novel LLM-based model tailored for financial time series forecasting and asset surge prediction, highlighting its minimal training needs and ability to capture complex dependencies.

Findings

BreakGPT effectively predicts asset surges in volatile markets.

It outperforms traditional models in capturing local and global temporal dependencies.

The model requires minimal training data.

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.