REPOGEO REPORT · LITE
Time-MoE/Time-MoE
Default branch main · commit 915bfda4 · scanned 5/31/2026, 12:13:25 AM
GitHub: 971 stars · 113 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 Time-MoE/Time-MoE, 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#1Add a direct, concise opening sentence to the README
Why:
COPY-PASTE FIXTime-MoE is the first billion-scale time series foundation model, leveraging a Mixture-of-Experts architecture for universal forecasting. This repository provides the official implementation and the largest open-access time series dataset, Time-300B.
- mediumabout#2Refine the repository description for clearer AI categorization
Why:
CURRENT[ICLR 2025 Spotlight] Official implementation of "Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts"
COPY-PASTE FIXTime-MoE: The first billion-scale time series foundation model with Mixture of Experts, enabling universal forecasting. Official ICLR 2025 Spotlight implementation.
- mediumreadme#3Add a 'Why Time-MoE?' section to the README
Why:
COPY-PASTE FIX## Why Time-MoE? Time-MoE stands out as the **first work** to scale time series foundation models up to **2.4 billion** parameters, trained from scratch. Unlike existing solutions, our Mixture-of-Experts architecture combined with the **Time-300B** dataset (the largest open-access time series data collection) provides unparalleled capabilities for universal forecasting with arbitrary prediction horizons and context lengths.
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.
- aws-samples/amazon-chronos · recommended 1×
- Nixtla/lag-llama · recommended 1×
- TimeGPT · recommended 1×
- huggingface/transformers · recommended 1×
- GPT-2 · recommended 1×
- CATEGORY QUERYHow to build or use large-scale foundation models for time series forecasting?you: not recommendedAI recommended (in order):
- Chronos (aws-samples/amazon-chronos)
- Lag-Llama (Nixtla/lag-llama)
- TimeGPT
- Hugging Face Transformers Library (huggingface/transformers)
- GPT-2
- BART
- T5
- PyTorch Forecasting (jdb78/pytorch-forecasting)
- GluonTS (awslabs/gluon-ts)
- Google Cloud Vertex AI
AI recommended 10 alternatives but never named Time-MoE/Time-MoE. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat deep learning techniques scale time series models to billions of parameters effectively?you: not recommendedAI recommended (in order):
- Hugging Face Transformers library
- PyTorch Lightning
- TensorFlow/Keras
- PyTorch
- JAX/Flax
- DeepSpeed (Microsoft)
- PyTorch Geometric (PyG)
- Deep Graph Library (DGL)
- PyTorch Distributed Data Parallel (DDP)
- Fully Sharded Data Parallel (FSDP)
- TensorFlow Distributed Strategy API
- NVIDIA Apex
- Google Cloud TPUs
AI recommended 13 alternatives but never named Time-MoE/Time-MoE. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- 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 Time-MoE/Time-MoE?passAI named Time-MoE/Time-MoE explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts Time-MoE/Time-MoE in production, what risks or prerequisites should they evaluate first?passAI named Time-MoE/Time-MoE 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 Time-MoE/Time-MoE solve, and who is the primary audience?passAI named Time-MoE/Time-MoE explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
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Live preview[](https://repogeo.com/en/r/Time-MoE/Time-MoE)<a href="https://repogeo.com/en/r/Time-MoE/Time-MoE"><img src="https://repogeo.com/badge/Time-MoE/Time-MoE.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
Time-MoE/Time-MoE — 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