Unveiling the Potential of Text in High-Dimensional Time Series Forecasting
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915100745531392 |
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| author | Zhou, Xin Wang, Weiqing Qu, Shilin Zhang, Zhiqiang Bergmeir, Christoph |
| author_facet | Zhou, Xin Wang, Weiqing Qu, Shilin Zhang, Zhiqiang Bergmeir, Christoph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07048 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unveiling the Potential of Text in High-Dimensional Time Series Forecasting Zhou, Xin Wang, Weiqing Qu, Shilin Zhang, Zhiqiang Bergmeir, Christoph Artificial Intelligence 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. |
| title | Unveiling the Potential of Text in High-Dimensional Time Series Forecasting |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2501.07048 |