TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models

Time-series foundation models provide strong zero-shot forecast, but deployment for real-world applications still requires task-specific designs for model inputs and outputs.

By Liu, Fu, Fu +4Oct 14, 2026

Score████░░░░░░3.7

Caveats

Preprint; not yet peer reviewed.

VerdictWorth a reader's time today.

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Abstract

Time-series foundation models provide strong zero-shot forecast, but deployment for real-world applications still requires task-specific designs for model inputs and outputs. Direct inference can leave domain knowledge and recurring forecast errors unaddressed, while tailored pipelines require substantial human expertise and effort. We introduce TimeRSI, a self-improving agentic deployment system around frozen forecasting models. A coding agent translates task descriptions into executable pipeline changes. It tests each revision on historical forecasting windows and uses observed errors to guide subsequent revisions. During deployment, an adaptive selector chooses among the resulting pipelines using completed forecasts under similar conditions. Across 198 tasks on TIME and fev-bench, TimeRSI outperforms all submissions on six point and probabilistic forecasting metrics. Controlled comparisons support the value of feedback-guided revision over non-iterative pipeline search. Further experiments verify the generalization with different coding agents and forecasting models. Code is available at https://github.com/haoxin1998/TimeRSI.

Haoxin Liu, Deqing Fu, Lucheng Fu, Yiqiao Jin, Rushi Qiang, Harshavardhan Kamarthi, B. Aditya Prakash

The editor's rubric

Heuristic review

Leverage4 / 5: A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).; 24% of this desk’s rubric
Magnitude3 / 5: Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.; 18% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 14% of this desk’s rubric
Novelty2 / 5: A new combination of known ideas.; 16% of this desk’s rubric
Trajectory3 / 5: A clear path to scale.; 18% of this desk’s rubric
Stakes2 / 5: Benefits a professional community (practitioners, clinicians, engineers).; 10% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	████░ 4	24%	A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).
Magnitude	███░░ 3	18%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	███░░ 3	14%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░ 2	16%	A new combination of known ideas.
Trajectory	███░░ 3	18%	A clear path to scale.
Stakes	██░░░ 2	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); breadth (many tasks, zero/few-shot); gains (outperforms); verification (multiple benchmarks, code released); stakes (general AI).

How the score was computed

rank-2026-10-18

Score████░░░░░░3.7

Score = 10 × (65% × adjusted merit / 10 + 25% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 6.0 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.8 / 10; Shrunk toward the desk prior by editor confidence (42%).
Attention: 11%; Citations, upvotes, points, mentions.
Freshness: 27%; Half-life decay since publication.

Citations0 (reference 15, via semantic-scholar, Oct 17, 2026, 13:49 UTC)
Influential citations0 (reference 3, via semantic-scholar, Oct 17, 2026, 13:49 UTC)
Hugging Face upvotes3 (reference 25, via hf-daily, Oct 18, 2026, 13:49 UTC)
GitHub stars3 (reference 250, via hf-daily, Oct 18, 2026, 13:49 UTC)

The record

Reviewed by heuristic-v7 on Oct 18, 2026, 13:49 UTC. Paper type: method.
Categories: cs.LG
BRIEF, No.10 in the Artificial Intelligence edition of October 19, 2026.