lixus7/Time-Series-Works-Conferences

• highreadme#1

  Add a clear 'Purpose' section at the top of the README

  原因：

  当前

  The README currently starts with '## About Me (Du Yin)' after the main title.

  复制粘贴的修复

  复制

  Add a section immediately after the main title, e.g., '## Purpose
  This repository serves as a comprehensive, curated summary of recent and impactful time-series research works presented at top computer science conferences (e.g., NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE). It aims to provide researchers and students with a centralized resource for understanding cutting-edge advancements in time-series analysis and deep learning.'

  待办完成忽略
• mediumhomepage#2

  Add a homepage URL to the repository metadata

  原因：

  复制粘贴的修复

  复制

  https://github.com/lixus7/Time-Series-Works-Conferences

  待办完成忽略
• lowtopics#3

  Add more descriptive topics to clarify the repo's nature as a research summary

  原因：

  当前

  accident-detection, anomaly-detection, deep-learning, demand-forecasting, location, multivariate-timeseries, paper-list, probabilistic-models, spatio-temporal, spatio-temporal-data, spatio-temporal-modeling, spatio-temporal-prediction, time-series, time-series-forecasting, time-series-imputation, time-series-prediction, traffic-prediction, travel-time-prediction

  复制粘贴的修复

  复制

  accident-detection, anomaly-detection, deep-learning, demand-forecasting, location, literature-review, multivariate-timeseries, paper-list, probabilistic-models, research-summary, spatio-temporal, spatio-temporal-data, spatio-temporal-modeling, spatio-temporal-prediction, time-series, time-series-forecasting, time-series-imputation, time-series-prediction, traffic-prediction, travel-time-prediction

  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 a summary of recent time series research from top conferences?

  你：未被推荐
  AI 推荐顺序：

  1. Papers With Code
  2. NeurIPS
  3. ICML
  4. ICLR
  5. KDD
  6. ACM Digital Library
  7. AAAI
  8. IJCAI
  9. AAAI Digital Library
  10. arXiv.org
  11. GitHub
  12. The Batch
  13. DeepLearning.AI
  14. Two Minute Papers
  15. Yannic Kilcher
  16. Twitter
  17. LinkedIn
  18. Google Scholar
  19. Semantic Scholar

  AI 推荐了 19 个替代方案，却始终没点名 lixus7/Time-Series-Works-Conferences。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 spatio-temporal data and forecasting?

  你：未被推荐
  AI 推荐顺序：

  1. DCRNN
  2. STGCN
  3. ASTGCN
  4. Informer
  5. Autoformer
  6. PatchTST
  7. FNO
  8. PINNs

  AI 推荐了 8 个替代方案，却始终没点名 lixus7/Time-Series-Works-Conferences。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 lixus7/Time-Series-Works-Conferences?

  pass
  ⚠AI 未点名 lixus7/Time-Series-Works-Conferences —— 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？

• If a team adopts lixus7/Time-Series-Works-Conferences in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 lixus7/Time-Series-Works-Conferences

  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 lixus7/Time-Series-Works-Conferences solve, and who is the primary audience?

  pass
  ⚠AI 未点名 lixus7/Time-Series-Works-Conferences —— 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？