REPOGEO REPORT · LITE
DaoSword/Time-Series-Forecasting-and-Deep-Learning
Default branch main · commit aaf53bf0 · scanned 5/30/2026, 4:28:01 PM
GitHub: 789 stars · 68 forks
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 DaoSword/Time-Series-Forecasting-and-Deep-Learning, 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.
- highreadme#1Reposition README H1 and opening paragraph to emphasize "curated list"
Why:
CURRENT# Time Series Forecasting and Deep Learning List of research papers focus on time series forecasting and deep learning, as well as other resources like competitions, datasets, courses, blogs, code, etc.
COPY-PASTE FIX# Awesome Time Series Forecasting and Deep Learning Resources A curated and comprehensive list of research papers, competitions, datasets, courses, blogs, code, and other valuable resources focused on time series forecasting and deep learning.
- mediumlicense#2Add a LICENSE file to the repository
Why:
CURRENT(no LICENSE file detected — the repo has no recognizable license)
COPY-PASTE FIXCreate a LICENSE file (e.g., MIT, Apache-2.0, or CC-BY-4.0 for content) in the repository root.
- lowhomepage#3Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIXAdd a relevant URL (e.g., a GitHub Pages site for the list, or a related project page) to the 'Homepage' field in the repository settings.
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.
- arXiv.org · recommended 1×
- Google Scholar · recommended 1×
- Papers With Code · recommended 1×
- NeurIPS (Conference on Neural Information Processing Systems) Proceedings · recommended 1×
- ICML (International Conference on Machine Learning) Proceedings · recommended 1×
- CATEGORY QUERYWhere can I find recent research papers on deep learning for time series prediction?you: not recommendedAI recommended (in order):
- arXiv.org
- Google Scholar
- Papers With Code
- NeurIPS (Conference on Neural Information Processing Systems) Proceedings
- ICML (International Conference on Machine Learning) Proceedings
- KDD (ACM SIGKDD Conference on Knowledge Discovery and Data Mining) Proceedings
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
- Pattern Analysis and Machine Intelligence (TPAMI)
AI recommended 8 alternatives but never named DaoSword/Time-Series-Forecasting-and-Deep-Learning. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are good resources for learning time series analysis and deep learning models?you: not recommendedAI recommended (in order):
- Forecasting: Principles and Practice
- Deep Learning for Time Series Forecasting
- Deep Learning
- Time Series Analysis and Forecasting with Python
- statsmodels
- TensorFlow
- Keras
- Practical Time Series Analysis
- Kaggle Learn Courses
AI recommended 9 alternatives but never named DaoSword/Time-Series-Forecasting-and-Deep-Learning. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 DaoSword/Time-Series-Forecasting-and-Deep-Learning?passAI named DaoSword/Time-Series-Forecasting-and-Deep-Learning explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts DaoSword/Time-Series-Forecasting-and-Deep-Learning in production, what risks or prerequisites should they evaluate first?passAI named DaoSword/Time-Series-Forecasting-and-Deep-Learning 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 DaoSword/Time-Series-Forecasting-and-Deep-Learning solve, and who is the primary audience?passAI did not name DaoSword/Time-Series-Forecasting-and-Deep-Learning — 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 DaoSword/Time-Series-Forecasting-and-Deep-Learning. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
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DaoSword/Time-Series-Forecasting-and-Deep-Learning — 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