RRepoGEO

REPOGEO REPORT · LITE

winedarksea/AutoTS

Default branch master · commit 49153938 · scanned 5/26/2026, 10:37:12 AM

GitHub: 1,411 stars · 123 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 winedarksea/AutoTS, 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
    Refine README's opening sentence to highlight 'automated' and 'scalable'

    Why:

    CURRENT
    AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale.
    COPY-PASTE FIX
    AutoTS is an automated time series forecasting package for Python, designed for rapidly deploying high-accuracy predictions at scale.
  • hightopics#2
    Add specific topics for 'automated machine learning' and 'scalable forecasting'

    Why:

    CURRENT
    automl, autots, deep-learning, feature-engineering, forecasting, machine-learning, preprocessing, time-series
    COPY-PASTE FIX
    automl, autots, deep-learning, feature-engineering, forecasting, machine-learning, preprocessing, time-series, automated-machine-learning, scalable-forecasting
  • mediumhomepage#3
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://winedarksea.github.io/AutoTS/

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 winedarksea/AutoTS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
facebook/prophet
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. facebook/prophet · recommended 1×
  2. awslabs/autogluon · recommended 1×
  3. pycaret/pycaret · recommended 1×
  4. sktime/sktime · recommended 1×
  5. Google Cloud Vertex AI Forecasting · recommended 1×
  • CATEGORY QUERY
    How can I automate high-accuracy time series forecasting for multiple variables?
    you: not recommended
    AI recommended (in order):
    1. Prophet (facebook/prophet)
    2. AutoGluon-Tabular (awslabs/autogluon)
    3. PyCaret (pycaret/pycaret)
    4. sktime (sktime/sktime)
    5. Google Cloud Vertex AI Forecasting
    6. Amazon Forecast
    7. StatsForecast (Nixtla/statsforecast)
    8. NeuralForecast (Nixtla/neuralforecast)
    9. MLForecast (Nixtla/mlforecast)

    AI recommended 9 alternatives but never named winedarksea/AutoTS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries offer automated machine learning for scalable time series predictions?
    you: not recommended
    AI recommended (in order):
    1. AutoGluon-TimeSeries
    2. TPOT
    3. PyCaret
    4. Prophet
    5. scikit-learn
    6. GridSearchCV
    7. RandomizedSearchCV
    8. Optuna
    9. Hyperopt
    10. MLflow

    AI recommended 10 alternatives but never named winedarksea/AutoTS. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 winedarksea/AutoTS?
    pass
    AI did not name winedarksea/AutoTS — 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?

  • If a team adopts winedarksea/AutoTS in production, what risks or prerequisites should they evaluate first?
    pass
    AI named winedarksea/AutoTS 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 winedarksea/AutoTS solve, and who is the primary audience?
    pass
    AI named winedarksea/AutoTS 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 winedarksea/AutoTS. 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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MARKDOWN (README)
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winedarksea/AutoTS — 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