A dataset and benchmark for forecasting real-world time series with cause-driven irregularities and multimodal observations.

Multivariate processing of diverse sensing modalities is limiting; we propose an adaptive framework to conduct explicit intra- and cross-modal learning using sparse attention that can handle arbitrary modality-missingness adeptly.