用大模型预测资产价格暴涨，仅需少量训练即可捕捉市场长期短期变化。
BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges
- 将大模型与时间序列学习结合，构建专用于金融波动预测的新架构。
- 在多个数据集上表现优于传统Transformer模型，能有效识别价格突增信号。
- 适合量化交易、金融风控等需要快速响应的场景，对小样本数据友好。
本文提出BreakGPT，一种专为时间序列预测和资产价格剧烈上涨事件建模而设计的大语言模型架构。通过融合大语言模型与基于Transformer的建模能力，研究评估了BreakGPT及其他Transformer模型在高波动金融市场中的表现。核心贡献在于验证了时间序列表征学习与大模型预测框架相结合的有效性。实验表明，BreakGPT可在极低训练成本下实现高效预测，是捕捉局部与全局时间依赖关系的强大工具，在多个金融数据集上展现出优于基准模型的性能。
原文摘要 · Abstract (English)
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.
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