The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
SCENARIODIFF is a hierarchical contextual reasoning framework designed for multimodal time series forecasting, especially in event-driven domains. It processes textual context through three agents—Historical Context, Scenario, and Anchor Guidance—to generate structured signals that condition a Multimodal Diffusion Transformer. The framework also employs Anchor Blended Sampling to locally refine forecast trajectories without retraining, and demonstrates superior performance on the Time‑MMD benchmark.
arXiv:2609.15087v1 Announce Type: cross Abstract: Most time series forecasting benchmarks remain numerical-centric and provide limited support for evaluating contextual information that shapes real-w...
The paper introduces TiMi, a framework that enhances time series transformers with a Multimodal Mixture-of-Experts (MMoE) module to incorporate multimodal data, especially textual information, into forecasting. TiMi leverages large language models to generate future inferences that guide predictions, eliminating the need for explicit representation alignment. Experiments show TiMi achieves state‑of‑the‑art performance on sixteen real‑world multimodal forecasting benchmarks, outperforming advanced baselines while maintaining adaptability and interpretability.
arXiv:2603. 05997v2 Announce Type: replace-cross Abstract: Irregularly sampled time series (ISTS) are widespread in real-world scenarios, exhibiting asynchronous observations on uneven time intervals across diverse variables.
arXiv:2603. 12451v4 Announce Type: replace Abstract: Context-aided forecasting (CAF) holds promise for integrating domain knowledge and forward-looking information, enabling AI systems to surpass traditional statistical methods.
MACTS-EM is a new multi‑agent framework for time series forecasting that combines domain‑specialised agents, a meta‑cognitive allocation layer, emergent memory for cross‑domain transfer, multimodal context integration, and adversarial robustness. The authors evaluate the system on financial, climate, energy, and pandemic data, reporting 8‑12% higher accuracy, 22‑27% better zero‑shot transfer, 16‑21% greater resilience to regime shifts, and 15‑18% faster recovery from distribution changes compared to existing methods. These results suggest that collaborative, agent‑based approaches can outperform traditional architectures in complex, real‑world forecasting tasks.