Unveiling the Potential of Text in High-Dimensional Time Series Forecasting

TL;DR

This paper introduces a novel framework that combines time series data with textual information using Large Language Models, significantly enhancing high-dimensional forecasting accuracy.

Contribution

It presents a new dual-tower multimodal approach integrating text and time series data, which improves forecasting performance over traditional methods.

Findings

Textual data improves forecasting accuracy

The dual-tower model outperforms baseline models

Framework demonstrates effectiveness on high-dimensional data

Abstract

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.