用文本增强高维时间序列预测，提升模型表现。
Unveiling the Potential of Text in High-Dimensional Time Series Forecasting
- 双塔结构融合时间序列与文本数据
- 实验表明加入文本可显著提升预测性能
- 适合关注多模态时序建模的研究者
时间序列预测传统上聚焦于单变量和多变量数值数据，常忽视融合多模态信息（尤其是文本数据）的潜力。本文提出一种新框架，将时间序列模型与大语言模型结合，以提升高维时间序列预测性能。受多模态模型启发，方法采用双塔结构融合时间序列与文本数据，生成综合表征，再通过线性层输出最终预测结果。大量实验表明，引入文本能有效提升高维时间序列预测效果。该工作为多模态时间序列预测研究开辟了新方向。
原文摘要 · Abstract (English)
Time series forecasting has traditionally focused on univariate and multivariate numerical data, often overlooking the benefits of incorporating multimodal information, particularly textual data. In this paper, we propose a novel framework that integrates time series models with Large Language Models to improve high-dimensional time series forecasting. Inspired by multimodal models, our method combines time series and textual data in the dual-tower structure. This fusion of information creates a comprehensive representation, which is then processed through a linear layer to generate the final forecast. Extensive experiments demonstrate that incorporating text enhances high-dimensional time series forecasting performance. This work paves the way for further research in multimodal time series forecasting.
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