Time-MMD
PulseAugur coverage of Time-MMD — every cluster mentioning Time-MMD across labs, papers, and developer communities, ranked by signal.
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Study questions text sensitivity in multimodal time-series forecasting models
A new research paper questions the effectiveness of text integration in multimodal time-series forecasting models. The study found that models like Aurora, MM-TSFlib, and TaTS do not significantly improve performance ba…
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New multimodal AI framework improves influenza forecasting accuracy
Researchers have developed a novel multimodal deep learning framework called Dual-Stream Attention (DSA) for forecasting influenza-like illness (ILI) up to 12 weeks in advance. This framework effectively integrates nume…
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New framework SCENARIODIFF improves multimodal time series forecasting
Researchers have introduced SCENARIODIFF, a novel framework designed to enhance multimodal time series forecasting by integrating textual context. This hierarchical approach organizes information into distinct agents: a…
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SCENARIODIFF framework enhances multimodal time series forecasting with scenario guidance
SCENARIODIFF is a new framework designed for multimodal time series forecasting, particularly effective when external events influence future dynamics. It structures contextual information from documents into three leve…
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LLM as Forecasting Planner framework integrates LLMs with TSFMs for improved forecasting
Researchers have developed a novel framework called LLM as Forecasting Planner (LAFP) that integrates large language models (LLMs) with time-series foundation models (TSFMs) for improved forecasting. This training-free …
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LLM as Forecasting Planner framework integrates LLMs with TSFMs for improved forecasting
Researchers have developed a novel framework called LLM as Forecasting Planner (rc) that integrates large language models (LLMs) with time-series foundation models (TSFMs) for improved text-conditioned forecasting. This…