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

Indar Kumar; Akanksha Tiwari; Sai Krishna Jasti; Ankit Hemant Lade

cs.LGMar 29, 2026

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

Organizations: independent researchers

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.

Figures & tables

Fig. 1: RG-TTA system overview. For each incoming batch

B_{t}

, the system extracts a distributional feature vector

\mathbf{r}

(Eq. 1 ) and computes a similarity score sim against stored regimes using a four-metric ensemble (Eq. 6 ). If a high-similarity checkpoint passes the loss gate (

\ell_{\text{ckpt}}<0.70\,\ell_{\text{curr}}

, Eq. 8 ), it replaces the current model. The learning rate is smoothly modulated by similarity (Eq. 7 ), and the output head is adapted with loss-driven early stopping (max 25 steps, patience 3). The adapted checkpoint and its regime features are stored in the memory

\mathcal{M}

(dashed box; 5 slots, FIFO eviction) for future reuse. See Algorithm 1 for pseudocode.

Key	Architecture	Parameters	Family
gru_small	2-layer GRU + MLP head	${\sim}71$ K	Recurrent
itransformer	iTransformer [ 18 ]	${\sim}123$ K	Attention
patchtst	PatchTST [ 19 ]	${\sim}192$ K	Attention
dlinear	DLinear [ 20 ]	${\sim}37$ K– $1.2$ M	Linear

TABLE I: Model architectures used in experiments. All policies use the same model per experiment.

#	Policy	Steps $K$	LR	Regime mem.	Key mechanism
1	TTA	20 (fixed)	$3{\times}10^{-4}$	No	Fixed-step gradient adaptation
2	EWC	15 (fixed)	$3{\times}10^{-4}$	No	Fisher-penalised adaptation
3	DynaTTA	20 (fixed)	Dynamic	No	Sigmoid LR from shift metrics
4	RG-TTA	$\leq$ 25 (early stop)	Sim-scaled	Yes	Continuous regime-guided TTA
5	RG-EWC	$\leq$ 25 (early stop)	Sim-scaled	Yes	+ EWC regularisation ( $\lambda$ =400)
6	RG-DynaTTA	$\leq$ 25 (early stop)	DynaTTA+RG	Yes	+ DynaTTA sigmoid LR

TABLE II: The 6 update policies. Policies 1–3 are baselines; 4–6 are our contributions. “Regime mem.” indicates whether the policy maintains a checkpoint library.

Policy	Wins	Win Rate
TTA	46	20.5%
EWC	13	5.8%
DynaTTA	9	4.0%
RG-TTA	65	29.0%
RG-EWC	68	30.4%
RG-DynaTTA	23	10.3%

TABLE III: Win counts across 224 seed-averaged experiments. Regime-guided policies (ours) win 156/224 (69.6%).

Baseline	RG-Variant	$\Delta$ MSE (avg)	$\Delta$ MSE (med.)	RG Wins	$p$ -value
TTA	RG-TTA	$-5.7\%$	$-5.1\%$	150/224 (67.0%)	$1.0{\times}10^{-5}$ ***
EWC	RG-EWC	$-14.1\%$	$-10.0\%$	169/224 (75.4%)	$2.4{\times}10^{-11}$ ***
DynaTTA	RG-DynaTTA	$+0.5\%$	$-3.8\%$	139/224 (62.1%)	$6.8{\times}10^{-3}$ **

TABLE IV: Head-to-head: each baseline vs its regime-guided variant.

\Delta

MSE is average relative change (negative = RG-variant is better). Wins = experiments where the RG-variant achieves lower MSE.

p

-values from one-sided Wilcoxon signed-rank tests [ 31 ] with Bonferroni correction (

\alpha=0.05/3

Fig. 2: Pair-wise MSE change from adding regime-guidance. Negative values indicate improvement. All three pairs are statistically significant (Wilcoxon, Bonferroni-corrected).

Policy	GRU-S	iTransformer	PatchTST	DLinear
TTA	15,458	21,075	717,298	17,855
EWC	16,794	24,141	813,314	18,258
DynaTTA	16,076	20,808	874,257	17,185
RG-TTA	14,092	19,202	718,642	18,782
RG-EWC	14,047	18,851	721,305	18,752
RG-DynaTTA	16,787	22,619	868,742	16,739

TABLE V: Average MSE across 14 datasets

\times

4 horizons, by policy and model. Bold = best per column. Results averaged over 3 seeds. Synthetic datasets dominate due to scale; see Table VI for real-world results.

Fig. 3: Real-world MSE by model architecture and policy. RG-TTA and RG-EWC dominate on GRU-Small and iTransformer; DLinear benefits most from RG-DynaTTA.

Fig. 4: Demšar-style critical difference diagram (Nemenyi,

\alpha=0.05

). Connected policies are not significantly different. RG-TTA and RG-EWC rank best (2.46, 2.51).

Policy	ETTh1	ETTh2	ETTm1	ETTm2	Weather	Exchange
TTA	56.15	92.54	20.88	40.53	138.99	0.01
EWC	63.80	102.21	23.40	42.89	147.67	0.01
DynaTTA	89.29	130.18	24.62	45.49	164.37	0.01
RG-TTA	53.74	78.99	18.05	37.69	145.21	0.01
RG-EWC	52.33	78.41	18.14	37.10	151.39	0.01
RG-DynaTTA	79.90	127.21	25.22	41.79	175.31	0.01

TABLE VI: Average MSE on real-world datasets (ETT, Weather, Exchange), averaged over 4 horizons, 4 models, and 3 seeds. Bold = best per column.

Policy	ETT (4)	Weather+Exch. (2)	Synth-Recurring (3)	Synth-Shock (5)
TTA	52.53	69.50	292,818	364,420
EWC	58.08	73.84	338,101	407,818
DynaTTA	72.40	82.19	366,484	429,847
RG-TTA	47.12	72.61	300,952	358,865
RG-EWC	46.50	75.70	303,245	359,054
RG-DynaTTA	68.53	87.66	357,825	432,636

TABLE VII: Average MSE by dataset category and policy, averaged across models and horizons.

Fig. 5: Per-dataset win rate (%) by policy. Real-world datasets (top) and synthetic (bottom) show similar patterns: RG-TTA and RG-EWC dominate most datasets.

Policy	GRU-S	iTransf.	PatchTST	DLinear	Overall
TTA	106.3	33.5	381.1	16.1	134.3
EWC	253.2	42.3	312.8	17.0	156.4
DynaTTA	121.9	34.7	411.8	17.2	146.4
RG-TTA	118.7	38.7	335.7	14.5	126.9
RG-EWC	286.7	55.0	360.2	19.1	180.3
RG-DynaTTA	129.0	36.9	421.2	17.0	151.0

TABLE VIII: Average total adaptation time (seconds) per experiment, by policy and model. Lower is better.

Fig. 6: MSE vs adaptation time. Left: all 14 datasets; right: real-world only. RG-TTA achieves the best trade-off (lower MSE and lower time). Square markers = regime-guided policies.

Fig. 7: MSE scaling with forecast horizon. Left: all datasets; right: real-world only. RG-TTA and RG-EWC (solid lines) outperform baselines (dashed) across all horizons.

Sweep	Value	MSE	$\Delta$ vs Default
LR scale $\gamma$ (Eq. 7 )	0.0 (no modulation)	16,068	$+17.9\%$
	0.33	14,601	$+7.2\%$
	0.67	13,624	—
	1.0	12,881	$-5.5\%$
	1.5	11,966	$-12.2\%$
Early stopping	Fixed 20 steps	15,515	$+13.9\%$

TABLE IX: Hyperparameter sensitivity (MSE averaged over 4 datasets

\times

2 horizons

\times

3 seeds). Bold = default. TTA baseline MSE = 15,515.

Similarity Method	Relative MSE vs Ensemble
Feature distance only (v0 design)	+8.3%
KS only	+2.1%
Wasserstein only	+1.8%
Variance ratio only	+5.7%
Ensemble (KS=0.3, W=0.3, F=0.2, V=0.2)	Baseline

TABLE X: Ablation: similarity metric choice. The ensemble is more robust than any single metric.

Fig. 8: Adaptation behaviour on ETTh2 (GRU-Small, H=96). Top : batch MSE across policies. Middle : regime similarity with checkpoint threshold (dashed red) and diagnostic tier boundaries. Bottom : RG-TTA’s adaptive learning rate vs TTA’s fixed rate. Higher similarity → lower LR (conservative); lower similarity → higher LR (aggressive).

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Component	Parameter	Value
Data Pipeline
	Sequence length $L$	96
	Forecast horizons $H$	96, 192, 336, 720
	Batch size (streaming)	750
	Initial training size	720
	Max batches	10

Appendix

TABLE XI: Full hyperparameter configuration across all policies.

Dataset	Source	Rows	Frequency	Season length
ETTh1, ETTh2	[ 16 ]	17,420	Hourly	24
ETTm1, ETTm2	[ 16 ]	69,680	15-min	96
Weather	[ 17 ]	52,696	10-min	144
Exchange	[ 35 ]	7,588	Daily	5
synth_* (8)	Generated	10,000–25,200	Synthetic	50

Appendix

TABLE XII: Dataset summary.

Policy	H=96	H=192	H=336	H=720
TTA	176,830	187,295	193,082	214,479
EWC	203,296	211,448	218,073	239,690
DynaTTA	216,969	227,041	232,179	252,137
RG-TTA	170,795	184,887	191,360	223,676
RG-EWC	168,993	184,932	192,007	227,024
RG-DynaTTA	212,964	224,449	234,977	252,499

Appendix

TABLE XIII: Average MSE by horizon and policy (all 14 datasets, 4 models, 3 seeds).

Test-Time Adaptation

Test-time adaptation updates or recalibrates pretrained models at inference to handle distribution shifts, often without target labels.

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Oct 8, 2026

Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting

Test-time adaptation (TTA) methods for time-series forecasting update a deployed model, or a small adapter around it, from incoming ground truth. But the label of an

H

-step forecast exists only

H

steps later, and real data pipelines add further delay. We build a leakage-free harness in which the label of forecast origin

s

is released for updates only at step

s+d

with

d \ge H

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Test-time adaptation (TTA) has recently emerged as a promising approach for improving time series forecasting (TSF) under distribution shift. Existing TSF-TTA methods differ in how they utilize revealed targets, yet the resulting adaptation protocols remain heterogeneous and lack a clearly unified formulation. To address this issue, we revisit TSF-TTA from the perspective of protocol cleanliness and propose an adaptation protocol based solely on matured ground truth, yielding a more principled setting for adaptation. Under this protocol, we further diagnose existing adapters in the frequency domain and find that their prediction corrections often exhibit limited and weakly structured spectral modifications. Motivated by this diagnosis, we propose Frequency-Aware Calibration (FAC), a lightweight calibration method that directly parameterizes prediction corrections in the frequency domain. Across diverse datasets, forecasting horizons, and source forecasters, FAC achieves competitive and consistent performance while requiring substantially fewer trainable parameters than the compared TSF-TTA adapters.

Haochun Wang, Ruichen Xu, Georgios Kementzidis +3

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Test-Time Adaptation (TTA) aims to improve time series forecasting under distribution shifts by using limited observations revealed during inference. However, forecasting TTA must operate in a source-free online setting, where the adaptation signal is short, temporally correlated, and potentially noisy. Existing methods can therefore suffer from weak identifiability, error accumulation, and unstable long-horizon corrections when the revealed prefix is sparse or contaminated. To address these issues, we propose STEPS, a Smooth Temporal Error Propagation Solver for TTA in time-series forecasting. STEPS reformulates forecasting TTA as a Dirichlet Boundary Value Problem on a temporal manifold, where the revealed prefix error serves as the boundary condition for the unknown future error field. Then, STEPS solves a smooth and bounded correction field in prediction space: a Local Solver propagates prefix errors under temporal smoothness, a Global Solver retrieves stable cross-window error memory and Spatiotemporal Manifold Fusion (SMF) integrates both solutions into the final correction. Across six standard benchmarks and four frozen backbones, STEPS achieves an average relative MSE reduction of 26.82% over the zero-shot backbone, exceeding the strongest compared TTA baseline by 12.77%. Additional sparse prefix and contamination tests confirm the robustness of STEPS under limited and noisy prefixes.

Jiaqi Liu, Yifan Ouyang, Zhifei Song +2

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