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Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling

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arXiv:2605. 30376v1 Announce Type: new Abstract: Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical inter-channel dependencies, while channel-dependent models are expressive but remain ``dimension-bounded'', struggling to generalize across heterogeneous datasets.

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Authors: Haochen Yuan, Yichen Song, Yunbo Wang, Xiaokang Yang
Released: 2026-06-01 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, project, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.30376

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