arXiv:2510.03244v2 Announce Type: replace Abstract: Large time series foundation models often adopt channel-independent architectures to handle varying data dimensions, but this design ignores crucial cross-channel dependencies. Meanwhile, existing cross-modal methods predominantly rely on textual modalities, leaving the spatial pattern recognition capabilities of vision models underexplored for time series analysis. To address these limitations, we propose VFEM, a cross-modal forecasting model that leverages pre-trained large vision models (LVMs) to capture complex cross-variable patterns. VF
The proliferation of powerful large vision models (LVMs) and the increasing demand for sophisticated time series forecasting across various sectors are creating fertile ground for cross-modal fusion techniques.
This development indicates a maturation of AI capabilities, bridging traditionally separate modalities (vision and time series) to enhance predictive accuracy and reveal complex, previously hidden patterns in data.
The ability to integrate visual features into time series forecasting shifts the paradigm from purely statistical or sequence-based methods to a more comprehensive understanding that leverages spatial pattern recognition.
- · AI researchers
- · Data scientists
- · Financial services
- · Supply chain logistics
- · Traditional time series forecasting methods
- · Domain-specific, non-AI-driven analytics tools
Improved accuracy and robustness in multivariate time series predictions will lead to better decision-making in various industries.
The integration of LVMs for pattern extraction may accelerate the development of more general-purpose AI systems capable of cross-domain reasoning.
This could lead to new AI-driven product categories that combine visual data analysis with predictive analytics for novel applications in areas like predictive maintenance or climate modeling.
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