[2603.27814] RG-TTA: Regime-Guided Meta-Control for Test-Time Adaptation in Streaming Time Series

Computer Science > Machine Learning arXiv:2603.27814 (cs) [Submitted on 29 Mar 2026] Title:RG-TTA: Regime-Guided Meta-Control for Test-Time Adaptation in Streaming Time Series Authors:Indar Kumar, Akanksha Tiwari, Sai Krishna Jasti, Ankit Hemant Lade View a PDF of the paper titled RG-TTA: Regime-Guided Meta-Control for Test-Time Adaptation in Streaming Time Series, by Indar Kumar and 3 other authors View PDF HTML (experimental) Abstract:Test-time adaptation (TTA) enables neural forecasters to adapt to distribution shifts in streaming time series, but existing methods apply the same adaptation intensity regardless of the nature of the shift. We propose Regime-Guided Test-Time Adaptation (RG-TTA), a meta-controller that continuously modulates adaptation intensity based on distributional similarity to previously-seen regimes. Using an ensemble of Kolmogorov-Smirnov, Wasserstein-1, feature-distance, and variance-ratio metrics, RG-TTA computes a similarity score for each incoming batch and uses it to (i) smoothly scale the learning rate -- more aggressive for novel distributions, conservative for familiar ones -- and (ii) control gradient effort via loss-driven early stopping rather than fixed budgets, allowing the system to allocate exactly the effort each batch requires. As a supplementary mechanism, RG-TTA gates checkpoint reuse from a regime memory, loading stored specialist models only when they demonstrably outperform the current model (loss improvement >= 30%). RG-TTA is...