RRepoGEO

REPOGEO REPORT · LITE

amazon-science/chronos-forecasting

Default branch main · commit 32111085 · scanned 5/29/2026, 5:16:59 AM

GitHub: 5,379 stars · 643 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 amazon-science/chronos-forecasting, 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
    Elevate core capabilities (multivariate, covariates, zero-shot) to the README introduction

    Why:

    CURRENT
    The information is currently in the 'News' section: 'Chronos-2 released. It offers _zero-shot_ support for univariate, multivariate, and covariate-informed forecasting tasks.'
    COPY-PASTE FIX
    Add the following sentence immediately after the main title or in the first introductory paragraph of the README: 'Chronos offers state-of-the-art zero-shot support for univariate, multivariate, and covariate-informed forecasting tasks, leveraging large-scale pre-trained models to generalize across diverse time series data.'
  • mediumreadme#2
    Add a comparison section to highlight Chronos's foundation model advantage

    Why:

    COPY-PASTE FIX
    Create a new section in the README, for example, '### Why Chronos? The Foundation Model Advantage', and include text that explains how Chronos's pre-trained, zero-shot foundation model approach differs from and outperforms traditional time series methods (e.g., Prophet, XGBoost) by offering superior generalization and reducing the need for extensive task-specific training.
  • lowtopics#3
    Add 'zero-shot-learning' to repository topics

    Why:

    CURRENT
    artificial-intelligence, forecasting, foundation-models, huggingface, huggingface-transformers, large-language-models, llm, machine-learning, pretrained-models, time-series, time-series-forecasting, timeseries, transformers
    COPY-PASTE FIX
    artificial-intelligence, forecasting, foundation-models, huggingface, huggingface-transformers, large-language-models, llm, machine-learning, pretrained-models, time-series, time-series-forecasting, timeseries, transformers, zero-shot-learning

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 amazon-science/chronos-forecasting
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Prophet
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Prophet · recommended 1×
  2. XGBoost · recommended 1×
  3. LightGBM · recommended 1×
  4. PyTorch Forecasting · recommended 1×
  5. DeepAR · recommended 1×
  • CATEGORY QUERY
    What are effective methods for multivariate time series forecasting with external covariates?
    you: not recommended
    AI recommended (in order):
    1. Prophet
    2. XGBoost
    3. LightGBM
    4. PyTorch Forecasting
    5. DeepAR
    6. AutoGluon
    7. Statsmodels
    8. TensorFlow Probability
    9. Edward2

    AI recommended 9 alternatives but never named amazon-science/chronos-forecasting. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which foundation models offer zero-shot capabilities for time series prediction tasks?
    you: not recommended
    AI recommended (in order):
    1. TimeGPT-2
    2. Lag-Llama
    3. Chronos
    4. OpenAI GPT-4 / GPT-3.5
    5. Google Gemini
    6. Meta Llama 2 / Llama 3

    AI recommended 6 alternatives but never named amazon-science/chronos-forecasting. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 amazon-science/chronos-forecasting?
    pass
    AI named amazon-science/chronos-forecasting explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts amazon-science/chronos-forecasting in production, what risks or prerequisites should they evaluate first?
    pass
    AI named amazon-science/chronos-forecasting 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 amazon-science/chronos-forecasting solve, and who is the primary audience?
    pass
    AI named amazon-science/chronos-forecasting explicitly

    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 amazon-science/chronos-forecasting. 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/amazon-science/chronos-forecasting.svg)](https://repogeo.com/en/r/amazon-science/chronos-forecasting)
HTML
<a href="https://repogeo.com/en/r/amazon-science/chronos-forecasting"><img src="https://repogeo.com/badge/amazon-science/chronos-forecasting.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

amazon-science/chronos-forecasting — 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