Adaptive Information Routing for Multimodal Time Series Forecasting
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912784798711808 |
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| author | Seo, Jun Choe, Hyeokjun Bae, Seohui Park, Soyeon Ahn, Wonbin Lim, Taeyoon Kang, Junhyeok Han, Sangjun Lee, Jaehoon Kang, Dongwan Kim, Minjae Yoo, Sungdong Lee, Soonyoung |
| author_facet | Seo, Jun Choe, Hyeokjun Bae, Seohui Park, Soyeon Ahn, Wonbin Lim, Taeyoon Kang, Junhyeok Han, Sangjun Lee, Jaehoon Kang, Dongwan Kim, Minjae Yoo, Sungdong Lee, Soonyoung |
| contents | Time series forecasting is a critical task for artificial intelligence with numerous real-world applications. Traditional approaches primarily rely on historical time series data to predict the future values. However, in practical scenarios, this is often insufficient for accurate predictions due to the limited information available. To address this challenge, multimodal time series forecasting methods which incorporate additional data modalities, mainly text data, alongside time series data have been explored. In this work, we introduce the Adaptive Information Routing (AIR) framework, a novel approach for multimodal time series forecasting. Unlike existing methods that treat text data on par with time series data as interchangeable auxiliary features for forecasting, AIR leverages text information to dynamically guide the time series model by controlling how and to what extent multivariate time series information should be combined. We also present a text-refinement pipeline that employs a large language model to convert raw text data into a form suitable for multimodal forecasting, and we introduce a benchmark that facilitates multimodal forecasting experiments based on this pipeline. Experiment results with the real world market data such as crude oil price and exchange rates demonstrate that AIR effectively modulates the behavior of the time series model using textual inputs, significantly enhancing forecasting accuracy in various time series forecasting tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10229 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Information Routing for Multimodal Time Series Forecasting Seo, Jun Choe, Hyeokjun Bae, Seohui Park, Soyeon Ahn, Wonbin Lim, Taeyoon Kang, Junhyeok Han, Sangjun Lee, Jaehoon Kang, Dongwan Kim, Minjae Yoo, Sungdong Lee, Soonyoung Machine Learning Artificial Intelligence Time series forecasting is a critical task for artificial intelligence with numerous real-world applications. Traditional approaches primarily rely on historical time series data to predict the future values. However, in practical scenarios, this is often insufficient for accurate predictions due to the limited information available. To address this challenge, multimodal time series forecasting methods which incorporate additional data modalities, mainly text data, alongside time series data have been explored. In this work, we introduce the Adaptive Information Routing (AIR) framework, a novel approach for multimodal time series forecasting. Unlike existing methods that treat text data on par with time series data as interchangeable auxiliary features for forecasting, AIR leverages text information to dynamically guide the time series model by controlling how and to what extent multivariate time series information should be combined. We also present a text-refinement pipeline that employs a large language model to convert raw text data into a form suitable for multimodal forecasting, and we introduce a benchmark that facilitates multimodal forecasting experiments based on this pipeline. Experiment results with the real world market data such as crude oil price and exchange rates demonstrate that AIR effectively modulates the behavior of the time series model using textual inputs, significantly enhancing forecasting accuracy in various time series forecasting tasks. |
| title | Adaptive Information Routing for Multimodal Time Series Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.10229 |