MM-TS: Channel-Structured Vision-Language Modeling for Multivariate Time Series Forecasting

Multivariate time-series forecasting requires modeling temporal dynamics and evolving cross-channel dependencies. Recent LLM- and VLM-based forecasters adapt numerical sequences to pretrained foundation models through textualization, patch reprogramming, or visual reconstruction. However, adapting inputs to a pretrained backbone does not by itself provide explicit channel-relation guidance for temporal-token attention. We propose MM-TS, a channel-structured vision-language framework for multivariate time-series forecasting. MM-TS reuses global and local channel relations derived from each input window in two complementary ways: as visual tokens and as signed biases on temporal-token attention logits. Relations from the full-window magnitude spectrum form an image, while relations from successive frequency-axis patches form the video frames. A pretrained vision-language backbone jointly processes these visual tokens, time-series tokens, and textual prompts. To preserve channel-pair identity, local relations occupy diagonal patch blocks in the attention-bias matrix, whereas the global relation is broadcast to off-diagonal blocks. This dual use aligns each relation entry with the attention edges of its channel pair, enabling channel structure to guide information routing rather than remain an input-only representation. Evaluation covers eight standard benchmarks and ten additional TIME datasets spanning varied variables and sampling resolutions. Against nine baselines, MM-TS achieves the lowest horizon-averaged MSE on 14 of these 18 datasets. It also remains competitive with limited training data and achieves the lowest average MSE in five of six evaluated ETT transfer directions without target-domain weight updates. Controlled ablations support the complementary contributions of the two paths and the importance of channel-pair correspondence and input-dependent relations. Code is available at https://anonymous.4open.science/r/MM-TS-DACB/.