arXiv AI June 30, 2026 By Xingsheng Chen, Xianpei Mu, Deyu Yi, Yilin Yuan, Xingwei He, Bo Gao, Regina Zhang, Pietro Lio, Siu-Ming Yiu

UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration

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arXiv:2604. 16325v3 Announce Type: replace-cross Abstract: Multivariate time series forecasting is fundamental to numerous domains such as energy, finance, and environmental monitoring, where complex temporal dependencies and cross-variable interactions pose enduring challenges.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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arXiv:2408. 11336v3 Announce Type: replace Abstract: Climate change stands as one of the most pressing global challenges of the twenty-first century, with far-reaching consequences such as rising sea levels, melting glaciers, and increasingly extreme weather patterns.

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Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors

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