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

Researchers present VFEM, a cross-modal forecasting model that combines pre-trained vision models with time series data to improve multivariate forecasting by capturing cross-channel dependencies. The approach transforms time series into visual representations and uses cross-modal attention fusion, achieving competitive performance while training only 7.45% of total parameters.

VFEM addresses a fundamental limitation in current time series foundation models: their reliance on channel-independent architectures that fail to capture relationships between different variables in multivariate datasets. By leveraging pre-trained large vision models (LVMs) to process visual representations of time series data, the approach opens an unexplored frontier in cross-modal machine learning. The dual-branch architecture enables independent extraction of visual and temporal features before fusion, creating a complementary information flow that enriches forecasting accuracy.

This research builds on growing recognition that different modalities can enhance predictive capabilities. While prior cross-modal work focused heavily on text-time series fusion, VFEM demonstrates that spatial pattern recognition from vision models translates effectively to temporal forecasting problems. The efficiency gains—achieving competitive results while freezing the LVM and training only 7.45% of parameters—highlight practical advantages for deployment and resource constraints.

The implications span multiple domains. For financial markets and trading systems, improved multivariate forecasting could enhance price prediction and risk modeling. For IoT and sensor networks, better cross-variable pattern recognition strengthens anomaly detection and system optimization. The parameter-efficient approach makes advanced forecasting accessible to organizations with limited computational resources.

Future developments likely include exploring other visual encoding schemes, testing on domain-specific datasets, and investigating whether other modalities (audio, graphs) could similarly enhance temporal prediction. The work suggests that foundation model reuse across seemingly disparate tasks remains underexplored, potentially unlocking efficiency gains across AI applications.

• →VFEM uses vision models to capture cross-channel dependencies ignored by channel-independent time series architectures
• →The model achieves competitive forecasting performance while training only 7.45% of parameters, emphasizing efficiency
• →Cross-modal fusion of visual and temporal features provides complementary information for improved predictions
• →The approach demonstrates that spatial pattern recognition from vision models transfers effectively to time series analysis
• →Parameter-efficient training enables deployment of advanced forecasting methods across resource-constrained environments