arXiv:2607.06607v1 Announce Type: new 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-pha
The increasing complexity and scale of real-world datasets for AI, particularly in long-term forecasting, necessitates new methods to account for inherent distribution shifts.
Improving AI's ability to model and adapt to fundamental changes via 'regime-oriented' learning enhances its reliability and applicability in dynamic environments, from finance to climate modeling.
AI models will move beyond simple temporal shifts to explicitly address and adapt to distinct operational regimes within datasets, making them more robust and less prone to catastrophic failures.
- · AI algorithm developers
- · Predictive analytics companies
- · Industries relying on long-term forecasting
- · Data scientists
- · Legacy forecasting models
- · AI models without regime-awareness
More accurate and resilient AI systems capable of operating effectively in highly variable conditions.
Increased trust in AI-driven predictions and insights across critical infrastructure and strategic planning.
Acceleration of autonomous AI agents operating in complex, real-world environments with evolving rules and states.
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Read at arXiv cs.LG