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ARXIV:2603.27814 · TIME SERIES FORECASTING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.27814TIME SERIES FORECASTINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEIndar Kumar · Akanksha Tiwari · Sai Krishna Jasti · Ankit Hemant Lade · arXiv
A meta-controller that dynamically adjusts adaptation intensity for streaming time series forecasting models to improve accuracy and speed.
Opportunity summary
Pain A meta-controller that dynamically adjusts adaptation intensity for streaming time series forecasting models to improve accuracy and speed.
Evidence 35 refs | 3 sources | 33% coverage
Blocker Evidence unverified
A meta-controller that dynamically adjusts adaptation intensity for streaming time series forecasting models to improve accuracy and speed. We propose Regime-Guided Test-Time Adaptation (RG-TTA), a meta-controller that continuously modulates adaptation intensity based on distributional…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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…
Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A meta-controller that dynamically adjusts adaptation intensity for streaming time series forecasting models to improve accuracy and speed.
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10.48550/arXiv.2603.27814A meta-controller that dynamically adjusts adaptation intensity for streaming time series forecasting models to improve accuracy and speed.
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 model-agnostic and strategy-composable: it wraps any forecaster exposing train/predict/save/load interfaces and enhances any gradient-based TTA method. We demonstrate three compositions -- RG-TTA, RG-EWC, and RG-DynaTTA -- and evaluate 6 update policies (3 baselines + 3 regime-guided variants) across 4 compact architectures (GRU, iTransformer, PatchTST, DLinear), 14 datasets (6 real-world multivariate benchmarks + 8 synthetic regime scenarios), and 4 forecast horizons (96, 192, 336, 720) under a streaming evaluation protocol with 3 random seeds (672 experiments total). Regime-guided policies achieve the lowest MSE in 156 of 224 seed-averaged experiments (69.6%), with RG-EWC winning 30.4% and RG-TTA winning 29.0%. Overall, RG-TTA reduces MSE by 5.7% vs TTA while running 5.5% faster; RG-EWC reduces MSE by 14.1% vs standalone EWC.
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Proof status
unverified35 refs; 3 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
A meta-controller that dynamically adjusts adaptation intensity for streaming time series forecasting models to improve accuracy and speed. We propose Regime-Guided Test-Time Adaptation (RG-TTA), a meta-controller that continuously modulates adaptation intensity based on distrib...
METHOD
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-controll...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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 shi...
WHY NOW
Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A meta-controller that dynamically adjusts adaptation intensity for streaming time series forecasting models to improve accuracy and speed. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Time Series Forecasting moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A meta-controller that dynamically adjusts adaptation intensity for streaming time series forecasting models to improve accuracy and speed.
Segment
Time Series Forecasting
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Commercial read
7.0/10 public viability
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2/3 checks · 67%
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reason
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proof status
unverified
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confidence low
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Evidence coverage
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35 refs / 3 sources / 33% coverage
stale
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Build readiness
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passport absent
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Technical feasibility
partial
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Evidence
35 references, 3 sources, 33% evidence coverage.
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DEFENSIBILITY
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