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

Xin Zhou, Weiqing Wang, SHILIN QU, Zhiqiang Zhang, Christoph Bergmeir

Keywords: Time Series Forecasting; Multiple data sources; High-dimensional Time Series Analysis

TL;DR: To explore the potential of text for high-dimensional time series forecasting, we propose a novel framework that integrates time series models with Large Language Models to improve high-dimensional time series forecasting.

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.

Submission Number: 77