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papersTODAY 04:00 UTC

Unified benchmark targets multimodal time series forecasting with heterogeneous context

Researchers present a new benchmark for time series forecasting that moves beyond purely numeric data to include contextual signals such as text and other modalities. They argue existing multimodal benchmarks are limited in both the volume of data and the range of context they cover. The work is posted as an arXiv preprint in cs.AI and cs.LG.

arXiv contextual signals heterogeneous context multimodal benchmarks multimodal time series forecasting time-series-forecasting

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIBeyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context ↗TODAY 04:00 UTC

arXiv cs.LGBeyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context ↗TODAY 04:00 UTC