VIFO: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914072870518784 |
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| author | Wang, Yanlong Yu, Hang Xu, Jian Ma, Fei Zhang, Hongkang Feng, Tongtong Zhang, Zijian Huang, Shao-Lun Sun, Danny Dongning Zhang, Xiao-Ping |
| author_facet | Wang, Yanlong Yu, Hang Xu, Jian Ma, Fei Zhang, Hongkang Feng, Tongtong Zhang, Zijian Huang, Shao-Lun Sun, Danny Dongning Zhang, Xiao-Ping |
| contents | Large time series foundation models often adopt channel-independent architectures to handle varying data dimensions, but this design ignores crucial cross-channel dependencies. Concurrently, existing multimodal approaches have not fully exploited the power of large vision models (LVMs) to interpret spatiotemporal data. Additionally, there remains significant unexplored potential in leveraging the advantages of information extraction from different modalities to enhance time series forecasting performance. To address these gaps, we propose the VIFO, a cross-modal forecasting model. VIFO uniquely renders multivariate time series into image, enabling pre-trained LVM to extract complex cross-channel patterns that are invisible to channel-independent models. These visual features are then aligned and fused with representations from the time series modality. By freezing the LVM and training only 7.45% of its parameters, VIFO achieves competitive performance on multiple benchmarks, offering an efficient and effective solution for capturing cross-variable relationships in |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03244 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VIFO: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion Wang, Yanlong Yu, Hang Xu, Jian Ma, Fei Zhang, Hongkang Feng, Tongtong Zhang, Zijian Huang, Shao-Lun Sun, Danny Dongning Zhang, Xiao-Ping Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Large time series foundation models often adopt channel-independent architectures to handle varying data dimensions, but this design ignores crucial cross-channel dependencies. Concurrently, existing multimodal approaches have not fully exploited the power of large vision models (LVMs) to interpret spatiotemporal data. Additionally, there remains significant unexplored potential in leveraging the advantages of information extraction from different modalities to enhance time series forecasting performance. To address these gaps, we propose the VIFO, a cross-modal forecasting model. VIFO uniquely renders multivariate time series into image, enabling pre-trained LVM to extract complex cross-channel patterns that are invisible to channel-independent models. These visual features are then aligned and fused with representations from the time series modality. By freezing the LVM and training only 7.45% of its parameters, VIFO achieves competitive performance on multiple benchmarks, offering an efficient and effective solution for capturing cross-variable relationships in |
| title | VIFO: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.03244 |