RRepoGEO

REPOGEO REPORT · LITE

liaoyuhua/LLM4TS

Default branch main · commit 16743618 · scanned 5/29/2026, 2:42:44 AM

GitHub: 566 stars · 48 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface liaoyuhua/LLM4TS, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening paragraph to clarify its purpose as a curated collection

    Why:

    CURRENT
    This project collects the papers and codes of Large Language Models (LLMs) and Foundation Models (FMs) for Time Series (TS). Hope this project can help you to understand the LLMs and FMs for TS.
    COPY-PASTE FIX
    This repository is a curated collection of papers and open-source implementations focusing on Large Language Models (LLMs) and Foundation Models (FMs) applied to Time Series (TS). It serves as a comprehensive survey and resource for researchers and practitioners exploring this emerging field.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root directory of the repository, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with the project's intent for sharing collected papers and code links.
  • mediumtopics#3
    Refine repository topics to emphasize its nature as a curated list/survey

    Why:

    CURRENT
    deep-learning, foundation-models, large-language-models, survey, time-series
    COPY-PASTE FIX
    deep-learning, foundation-models, large-language-models, time-series, survey, research-papers, curated-list, awesome-list

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface liaoyuhua/LLM4TS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TimeGPT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TimeGPT · recommended 1×
  2. NeuralForecast · recommended 1×
  3. StatsForecast · recommended 1×
  4. OpenAI GPT-4 · recommended 1×
  5. OpenAI GPT-3.5 · recommended 1×
  • CATEGORY QUERY
    How can I leverage large language models for advanced time series analysis and forecasting?
    you: not recommended
    AI recommended (in order):
    1. TimeGPT
    2. NeuralForecast
    3. StatsForecast
    4. OpenAI GPT-4
    5. OpenAI GPT-3.5
    6. Google Vertex AI
    7. Google PaLM
    8. Google Gemini
    9. Hugging Face Transformers Library
    10. LangChain

    AI recommended 10 alternatives but never named liaoyuhua/LLM4TS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive survey of foundation models applied to time series data?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. NeurIPS
    4. ICML
    5. ICLR
    6. KDD
    7. AAAI
    8. IJCAI
    9. Google AI Blog
    10. Meta AI Blog
    11. Microsoft Research Blog
    12. Hugging Face Blog

    AI recommended 12 alternatives but never named liaoyuhua/LLM4TS. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of liaoyuhua/LLM4TS?
    pass
    AI named liaoyuhua/LLM4TS explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts liaoyuhua/LLM4TS in production, what risks or prerequisites should they evaluate first?
    pass
    AI named liaoyuhua/LLM4TS explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo liaoyuhua/LLM4TS solve, and who is the primary audience?
    pass
    AI named liaoyuhua/LLM4TS explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)
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HTML
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liaoyuhua/LLM4TS — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite