arXiv AI
Beyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context
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TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
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.
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TimeWorld: Towards Action-Conditioned Multimodal World Models for Time Series Forecasting
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Rethinking Multimodal Time-Series Forecasting Evaluation
arXiv:2607. 06973v1 Announce Type: new Abstract: We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX.
arXiv AI
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MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision
arXiv:2610.17856v2 Announce Type: cross Abstract: Time-series analysis is increasingly studied in multimodal settings, yet most benchmarks pair temporal signals with text or with visualizations deriv...
arXiv Machine Learning
Aug 19
SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting--Extended Version
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.
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SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting--Extended Version
Textual context such as news, reports, and logs can provide valuable signals for time series forecasting, especially when future dynamics are driven by external events that are not yet visible in hist...