RRepoGEO

REPOGEO REPORT · LITE

xiyuanzh/awesome-llm-time-series

Default branch main · commit f6bd79cb · scanned 5/31/2026, 9:13:20 AM

GitHub: 517 stars · 33 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 xiyuanzh/awesome-llm-time-series, 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 README H1 to clarify project type

    Why:

    CURRENT
    # awesome-llm-time-series
    COPY-PASTE FIX
    # awesome-llm-time-series: A Curated List and Survey of Large Language Models for Time Series Analysis
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    awesome-list, llm, time-series, machine-learning, deep-learning, survey, research, papers, datasets, models
  • mediumlicense#3
    Add a LICENSE file or clarify license in README

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root, or add a clear statement about the applicable license(s) directly in the README, for example: 'This project is licensed under the [License Name] - see the LICENSE.md file for details.'

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 xiyuanzh/awesome-llm-time-series
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Anthropic Claude 3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Anthropic Claude 3 · recommended 2×
  2. Google Gemini · recommended 2×
  3. huggingface/transformers · recommended 2×
  4. OpenAI GPT-4/GPT-3.5 Turbo · recommended 1×
  5. Llama 3 · recommended 1×
  • CATEGORY QUERY
    How can I leverage large language models for analyzing time series data effectively?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4/GPT-3.5 Turbo
    2. Anthropic Claude 3
    3. Google Gemini
    4. Hugging Face Transformers (huggingface/transformers)
    5. Llama 3
    6. Mistral Large

    AI recommended 6 alternatives but never named xiyuanzh/awesome-llm-time-series. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best techniques for integrating large language models with time series forecasting?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. Anthropic Claude 3
    3. Google Gemini
    4. OpenAI Embeddings
    5. Google Universal Sentence Encoder
    6. Hugging Face `sentence-transformers` (UKP-SQuARE/sentence-transformers)
    7. LightGBM (microsoft/LightGBM)
    8. XGBoost (dmlc/xgboost)
    9. Prophet (facebook/prophet)
    10. N-BEATS (ServiceNow/N-BEATS)
    11. DeepAR
    12. LLaMA-2 (facebookresearch/llama)
    13. Falcon
    14. Mistral (mistralai/mistral-src)
    15. T5 (google-research/text-to-text-transfer-transformer)
    16. GPT-2 (openai/gpt-2)
    17. Hugging Face Transformers (huggingface/transformers)
    18. `peft` (huggingface/peft)
    19. AutoGluon (awslabs/autogluon)
    20. PyTorch Forecasting (jdb78/pytorch-forecasting)
    21. GluonTS (awslabs/gluon-ts)
    22. Temporal Fusion Transformer (TFT) (google-research/tft)
    23. ARIMA
    24. Exponential Smoothing

    AI recommended 24 alternatives but never named xiyuanzh/awesome-llm-time-series. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 xiyuanzh/awesome-llm-time-series?
    pass
    AI did not name xiyuanzh/awesome-llm-time-series — likely talking about a different project

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

  • If a team adopts xiyuanzh/awesome-llm-time-series in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xiyuanzh/awesome-llm-time-series 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 xiyuanzh/awesome-llm-time-series solve, and who is the primary audience?
    pass
    AI did not name xiyuanzh/awesome-llm-time-series — likely talking about a different project

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

Embed your GEO score

Drop this badge into the README of xiyuanzh/awesome-llm-time-series. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/xiyuanzh/awesome-llm-time-series.svg)](https://repogeo.com/en/r/xiyuanzh/awesome-llm-time-series)
HTML
<a href="https://repogeo.com/en/r/xiyuanzh/awesome-llm-time-series"><img src="https://repogeo.com/badge/xiyuanzh/awesome-llm-time-series.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

xiyuanzh/awesome-llm-time-series — 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