用高阶注意力捕捉多源金融时序数据，提升股票走势预测效果
Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data
- 扩展自注意力机制至高阶，建模跨时间与变量的复杂动态
- 通过张量分解与核注意力，将计算复杂度降至线性
- 融合价格与社交媒体数据，适合量化交易与金融建模研究者
本文针对金融市场中的股票走势预测挑战，提出高阶Transformer架构，专为处理多变量时序数据设计。通过将自注意力机制和变压器结构扩展至高阶，有效捕捉跨时间与变量的复杂市场动态。为控制计算复杂度，采用张量分解对大型注意力张量进行低秩近似，并引入核注意力，使复杂度随数据规模呈线性增长。此外，我们构建了一个编码器-解码器模型，整合技术分析与基本面分析，利用历史价格与相关推文等多模态信号。在Stocknet数据集上的实验表明该方法有效，展现出在金融市场股票走势预测中的潜力。
原文摘要 · Abstract (English)
In this paper, we tackle the challenge of predicting stock movements in financial markets by introducing Higher Order Transformers, a novel architecture designed for processing multivariate time-series data. We extend the self-attention mechanism and the transformer architecture to a higher order, effectively capturing complex market dynamics across time and variables. To manage computational complexity, we propose a low-rank approximation of the potentially large attention tensor using tensor decomposition and employ kernel attention, reducing complexity to linear with respect to the data size. Additionally, we present an encoder-decoder model that integrates technical and fundamental analysis, utilizing multimodal signals from historical prices and related tweets. Our experiments on the Stocknet dataset demonstrate the effectiveness of our method, highlighting its potential for enhancing stock movement prediction in financial markets.
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