Unveiling the Potential of Text in High-Dimensional Time Series Forecasting
This work addresses the challenge of incorporating multimodal information for researchers in time series forecasting, though it appears incremental in combining existing methods.
The paper tackled the problem of high-dimensional time series forecasting by integrating textual data with time series models using a dual-tower structure, resulting in improved forecasting performance as demonstrated in experiments.
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.