AI RESEARCH
What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
arXiv CS.AI
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ArXi:2605.08289v1 Announce Type: cross Multivariate time series forecasting is critical in many real-world systems, and thus modeling cross-channel dependencies is essential. Although existing methods improve overall accuracy by enhancing representations and cross-channel interactions, it remains challenging to reliably capture inter-variable dependencies under specific conditions.