A cross-modal model that renders multivariate time series as images, encodes them with a frozen SigLip2 vision model, and fuses the features with a temporal attention branch achieves state-of-the-art results on seven forecasting benchmarks.
Problem Formulation As shown in Figure 2, the input multivariate time series is denoted asX ts ∈R M×L with look-back windowL: (x1, x2,
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VFEM: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion
A cross-modal model that renders multivariate time series as images, encodes them with a frozen SigLip2 vision model, and fuses the features with a temporal attention branch achieves state-of-the-art results on seven forecasting benchmarks.