VIFO: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion

• URL: http://arxiv.org/abs/2510.03244v1
• Date: Thu, 25 Sep 2025 14:02:26 GMT
• Title: VIFO: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion
• Authors: Yanlong Wang, Hang Yu, Jian Xu, Fei Ma, Hongkang Zhang, Tongtong Feng, Zijian Zhang, Shao-Lun Huang, Danny Dongning Sun, Xiao-Ping Zhang,
• Abstract summary: We propose VIFO, a cross-temporal forecasting model, for time series models.<n>It renders multivariate time series into image, enabling pre-trained LVM to extract complex cross-channel patterns.<n>It achieves competitive performance on multiple benchmarks.
• Score: 30.95449991386488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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