EviTime: Evidence-Guided Vision-Language Learning for Multimodal Time Series Forecasting

Multimodal time series forecasting aims to exploit complementary numerical, visual, and semantic information for more accurate predictions. However, existing methods often treat temporal observations as equally reliable, rely on limited visual representations to characterize temporal structures, and introduce generic textual descriptions that are weakly aligned with individual forecasting instances. To address these limitations, **EviTime** is proposed as an **Evi**dence-guided vision-language learning framework for multimodal **Time** series forecasting. First, EviTime introduces evidential reasoning into temporal representation learning by combining Dempster-Shafer Theory (DST), nonlinear Basic Probability Assignment (BPA) generation, and Deng entropy to characterize observation reliability and suppress ambiguous information. Second, numerical sequences are further transformed into three-channel visual representations using Gramian Angular Summation Fields (GASF), Gramian Angular Difference Fields (GADF), and amplitude information, enabling a pretrained Vision-Language Model (VLM) to capture complementary structural patterns. Third, hierarchical metadata prompts encode dataset-, task-, and sample-level semantics, while a cross-attention metadata router extracts instance-relevant language information. Temporal, visual, and semantic representations are then aligned and adaptively fused for forecasting. Finally, extensive experiments on multiple benchmarks demonstrate consistent improvements over competitive baselines, with Mean Squared Error (MSE) and Mean Absolute Error (MAE) reduced by 18.4% and 13.6%, respectively, compared with the average performance of 10 state-of-the-art methods. The source code is publicly available at https://anonymous.4open.science/r/EvidentialTime-CD2F.