PRISM: A Training-Free Visual Forecaster for Time Series

Vision-based time series forecasting provides a promising route for reusing pretrained visual models for temporal modeling. Existing approaches have explored increasingly rich visual representations, multimodal augmentation, learned temporal-visual alignment, and adaptation of pretrained vision models. However, input-format compatibility does not necessarily ensure structural compatibility with pretrained visual representations. A key issue therefore remains underexplored: how to organize complementary temporal factors in a way that is compatible with the model-specific geometry learned during visual pretraining? We term the resulting mismatch between temporal factor organization and the pretrained RGB geometry Time-Series-to-RGB (TS2RGB) misalignment. To address this problem, we propose Prism, a training-free forecasting model built on a frozen MAE encoder-decoder. Temporal Chromatic Rendering (TCR) organizes complementary temporal structures along mutually orthogonal RGB directions derived from the spatially constant color sensitivity of the frozen MAE patch-embedding layer, while Topology-Guided Visual Token Propagation (TVTP) injects dynamic cross-variable dependencies through spatially corresponding latent tokens. Prism introduces no additional trainable parameters and performs forecasting without downstream weight updates. Across eight multivariate benchmarks and four forecasting horizons, Prism ranks first in 43/64 dataset-horizon-metric comparisons and 12/16 dataset-metric averages, with real-token analyses showing 63.5% and 14.9% reductions in cross-component coupling at the patch-embedding interface and final encoder representation, respectively.