Two new research papers explore the complexities of multimodal time series forecasting, focusing on how to effectively integrate and evaluate textual context. The first paper introduces MUSE-Bench, a comprehensive benchmark with fourteen datasets and six context types, designed to assess various forecasting methods, including LLMs. It finds that while numerical models lead, external context can improve performance if aligned correctly, and general-purpose LLMs struggle as direct forecasters. The second paper addresses the challenge of determining whether text annotations truly contribute to forecasting accuracy by developing a benchmark to evaluate information-theoretic metrics. This research aims to provide principled methods for auditing annotation quality and selecting optimal text for fusion in forecasting models. AI
IMPACT Establishes new evaluation frameworks for multimodal forecasting, potentially guiding future research and development in context-aware time series prediction.
RANK_REASON Two academic papers published on arXiv introducing new benchmarks and evaluation methods for multimodal forecasting.
- arXiv
- Hugging Face
- InfoNCE
- MINE
- principal ideal domain
- V-information
- alphaXiv
- aurora
- CatalyzeX
- DagsHub
- Gotit.pub
- MUSE-Bench
- ScienceCast
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