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pith:DMUIVTJV

pith:2023:DMUIVTJVBFYOSVIZHZH443YPG6

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Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

James Y. Zhang, Lintao Ma, Ming Jin, Pin-Yu Chen, Qingsong Wen, Shirui Pan, Shiyu Wang, Xiaoming Shi, Yuan-Fang Li, Yuxuan Liang, Zhixuan Chu

Reprogramming time series inputs with text prototypes lets frozen large language models generate accurate forecasts without retraining.

arxiv:2310.01728 v2 · 2023-10-03 · cs.LG · cs.AI

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Claims

C1strongest claim

Our comprehensive evaluations demonstrate that Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models. Moreover, Time-LLM excels in both few-shot and zero-shot learning scenarios.

C2weakest assumption

That reprogramming time series inputs with text prototypes and Prompt-as-Prefix successfully aligns the modalities so the frozen LLM's reasoning transfers without substantial loss of temporal structure or introduction of artifacts.

C3one line summary

Time-LLM reprograms frozen LLMs for time series forecasting via text prototypes and Prompt-as-Prefix, outperforming specialized models in standard, few-shot, and zero-shot settings.

References

112 extracted · 112 resolved · 4 Pith anchors

[1] Kingma and Jimmy Ba , title =

[3] Advances in Neural Information Processing Systems , volume=

[4] IEEE Transactions on Neural Networks and Learning Systems , year=

[5] Advances in Neural Information Processing Systems , volume=

[7] Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , pages=

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Cited by

33 papers in Pith

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MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models

Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs

Receipt and verification

First computed	2026-05-17T23:38:47.368710Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1b288acd350970e955193e4fce6f0f37bb785b217462bafe1c85711d592a442a

Aliases

arxiv: 2310.01728 · arxiv_version: 2310.01728v2 · doi: 10.48550/arxiv.2310.01728 · pith_short_12: DMUIVTJVBFYO · pith_short_16: DMUIVTJVBFYOSVIZ · pith_short_8: DMUIVTJV

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DMUIVTJVBFYOSVIZHZH443YPG6 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1b288acd350970e955193e4fce6f0f37bb785b217462bafe1c85711d592a442a

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "d9d5c97e04950a3e8f51d224e0d54444d358a32b0977dc0d00a7860b4c28bdd8",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2023-10-03T01:31:25Z",
    "title_canon_sha256": "10c17c7b20a71707ea2554d79be47a18420a4edb84fc4f49444ea031e35f4983"
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    "kind": "arxiv",
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