NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts
Researchers propose NEST, a framework that addresses dataset-level distribution shifts by identifying distinct operational regimes via unsupervised clustering and using a regime-oriented mixture-of-experts architecture. It achieves state-of-the-art performance on long-term forecasting tasks across network traffic and physical phenomena benchmarks.
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[Submitted on 7 Jul 2026]
Title:NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts
View a PDF of the paper titled NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts, by Lanhao Li and 5 other authors
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Abstract:Accurate long-term forecasting in complex systems is frequently compromised by dataset-level distribution shifts, where diverse underlying behavioral modes and evolving system states drive the dynamic multivariate time-series. While existing methods predominantly focus on local temporal shifts, they fail to explicitly model the global structural challenge where datasets are composites of distinct operational regimes. In this paper, we propose NEST, a specialized framework designed to model and recompose these evolving structures through a two-phase dense MoE architecture. NEST first facilitates structural specialization by partitioning the dataset into distinct operational regimes through unsupervised clustering in a principled moment-entropy space. We introduce a regime-oriented router mechanism that generates initial expert weights based on temporal content, subsequently refined through geometric modulation to regime centroids. Crucially, rather than acting as monolithic predictors, individual experts function as specialized kernels that capture regime-specific dynamics by evolving unique variate-attention patterns. Extensive evaluations on diverse benchmarks, including heterogeneous network traffic and physical phenomena, demonstrate that NEST consistently achieves state-of-the-art performance. Our code and datasets are available at this https URL
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.06607 [cs.LG]
(or arXiv:2607.06607v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.06607
arXiv-issued DOI via DataCite
Submission history
From: Lanhao Li [view email] [v1] Tue, 7 Jul 2026 05:36:47 UTC (2,145 KB)
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