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

• highreadme#1

  Reposition README opening to emphasize definitive collection

  原因：

  当前

  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.

  复制粘贴的修复

  复制

  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.

  待办完成忽略
• mediumhomepage#2

  Add a homepage URL to repository settings

  原因：

  复制粘贴的修复

  复制

  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).

  待办完成忽略
• lowreadme#3

  Add a 'Why This List?' section to README

  原因：

  复制粘贴的修复

  复制

  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."

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Where can I find recent research papers and tutorials on AI for time series analysis?

  你：未被推荐
  AI 推荐顺序：

  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 推荐了 14 个替代方案，却始终没点名 qingsongedu/awesome-AI-for-time-series-papers。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What are the cutting-edge deep learning techniques for time series forecasting and anomaly detection?

  你：未被推荐
  AI 推荐顺序：

  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 推荐了 16 个替代方案，却始终没点名 qingsongedu/awesome-AI-for-time-series-papers。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of qingsongedu/awesome-AI-for-time-series-papers?

  pass
  ✓AI 明确点名了 qingsongedu/awesome-AI-for-time-series-papers

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts qingsongedu/awesome-AI-for-time-series-papers in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 qingsongedu/awesome-AI-for-time-series-papers

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo qingsongedu/awesome-AI-for-time-series-papers solve, and who is the primary audience?

  pass
  ⚠AI 未点名 qingsongedu/awesome-AI-for-time-series-papers —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？