RRepoGEO

REPOGEO REPORT · LITE

qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM

Default branch main · commit 70edb1de · scanned 6/24/2026, 5:03:51 PM

GitHub: 1,218 stars · 91 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
22 /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
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 qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM, 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.

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 qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. OpenAI GPT-4 / GPT-3.5 · recommended 1×
  3. Google Gemini · recommended 1×
  4. TimeGPT by Nixtla · recommended 1×
  5. Chronos by Amazon · recommended 1×
  • CATEGORY QUERY
    How can large language models be applied to time series analysis and forecasting tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5
    2. Google Gemini
    3. Hugging Face Transformers
    4. TimeGPT by Nixtla
    5. Chronos by Amazon

    AI recommended 5 alternatives but never named qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What foundation models are available for spatiotemporal data anomaly detection and predictive maintenance?
    you: not recommended
    AI recommended (in order):
    1. DeepAR
    2. Hugging Face Transformers
    3. PyTorch
    4. TensorFlow
    5. Time Series Transformers (TST)
    6. pytorch_forecasting
    7. Informer
    8. Autoformer
    9. FEDformer
    10. PyTorch Geometric
    11. DGL
    12. Graph Convolutional Recurrent Networks (GCRN)
    13. Spatio-Temporal Graph Convolutional Networks (STGCN)
    14. OmniAnomaly
    15. Prophet
    16. XGBoost
    17. LightGBM

    AI recommended 17 alternatives but never named qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM. 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 qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM?
    pass
    AI did not name qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM — 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 qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM 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 qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM solve, and who is the primary audience?
    pass
    AI did not name qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM — 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 qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM. 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/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM.svg)](https://repogeo.com/en/r/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM)
HTML
<a href="https://repogeo.com/en/r/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM"><img src="https://repogeo.com/badge/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM — 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