RRepoGEO

REPOGEO REPORT · LITE

qingsongedu/awesome-AI-for-time-series-papers

Default branch main · commit 8a67651d · scanned 6/18/2026, 5:48:14 PM

GitHub: 1,618 stars · 147 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)

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

AI VISIBILITY SCORE
28 /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
2 / 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-AI-for-time-series-papers, 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 opening to emphasize definitive collection

    Why:

    CURRENT
    A professionally curated list of papers (with available code), tutorials, and surveys on recent **AI for Time Series Analysis (AI4TS)**, including Time Series, Spatio-Temporal Data, Event Data, Sequence Data, Temporal Point Processes, etc., at the **Top AI Conferences and Journals**, which is **updated ASAP (the earliest time)** once the accepted papers are announced in the corresponding top AI conferences/journals. Hope this list would be helpful for researchers and engineers who are interested in AI for Time Series Analysis.
    COPY-PASTE FIX
    This repository is the definitive, professionally curated collection of papers (with available code), tutorials, and surveys on recent **AI for Time Series Analysis (AI4TS)**. Unlike searching broad platforms or individual conference proceedings, this list provides a focused, up-to-date resource from **Top AI Conferences and Journals**, updated ASAP upon paper announcements. It's designed to be the primary resource for researchers and engineers interested in AI for Time Series Analysis.
  • mediumhomepage#2
    Add a homepage URL to repository settings

    Why:

    COPY-PASTE FIX
    Set the 'Homepage' field in the repository settings to `https://qingsongedu.github.io/awesome-AI-for-time-series-papers/` (or similar GitHub Pages URL if enabled).
  • lowreadme#3
    Add a 'Why This List?' section to README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, perhaps titled "Why Use This List?" or "How This List Compares", with content like: "Unlike general search engines (e.g., Google Scholar, arXiv) or individual conference proceedings, this repository offers a meticulously curated and categorized collection focused exclusively on AI for Time Series. It saves you time by centralizing high-quality, peer-reviewed research from top-tier venues, often including available code, and is updated promptly."

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-AI-for-time-series-papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. Google Scholar · recommended 1×
  3. NeurIPS · recommended 1×
  4. ICML · recommended 1×
  5. ICLR · recommended 1×
  • CATEGORY QUERY
    Where can I find recent research papers and tutorials on AI for time series analysis?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. NeurIPS
    4. ICML
    5. ICLR
    6. KDD
    7. PMLR
    8. Papers With Code
    9. Towards Data Science
    10. Medium
    11. Kaggle
    12. YouTube
    13. StatQuest with Josh Starmer
    14. DeepLearning.AI

    AI recommended 14 alternatives but never named qingsongedu/awesome-AI-for-time-series-papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the cutting-edge deep learning techniques for time series forecasting and anomaly detection?
    you: not recommended
    AI recommended (in order):
    1. DeepAR
    2. Amazon SageMaker
    3. Temporal Fusion Transformers (TFT)
    4. PyTorch Forecasting
    5. Google Cloud AI Platform
    6. Informer
    7. Autoformer
    8. FEDformer
    9. PyTorch
    10. TensorFlow
    11. N-BEATS
    12. OmniAnomaly
    13. TranAD
    14. One-Class SVMs (OC-SVM)
    15. Isolation Forests
    16. Scikit-learn

    AI recommended 16 alternatives but never named qingsongedu/awesome-AI-for-time-series-papers. 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-AI-for-time-series-papers?
    pass
    AI named qingsongedu/awesome-AI-for-time-series-papers explicitly

    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-AI-for-time-series-papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named qingsongedu/awesome-AI-for-time-series-papers 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-AI-for-time-series-papers solve, and who is the primary audience?
    pass
    AI did not name qingsongedu/awesome-AI-for-time-series-papers — 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-AI-for-time-series-papers. 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-AI-for-time-series-papers.svg)](https://repogeo.com/en/r/qingsongedu/awesome-AI-for-time-series-papers)
HTML
<a href="https://repogeo.com/en/r/qingsongedu/awesome-AI-for-time-series-papers"><img src="https://repogeo.com/badge/qingsongedu/awesome-AI-for-time-series-papers.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

qingsongedu/awesome-AI-for-time-series-papers — 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