RRepoGEO

REPOGEO REPORT · LITE

functime-org/functime

Default branch main · commit 71e11992 · scanned 6/22/2026, 11:26:58 PM

GitHub: 1,180 stars · 64 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 functime-org/functime, 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
    Reposition README opening to highlight Polars and parallel processing

    Why:

    CURRENT
    functime is a powerful Python library for production-ready global forecasting and time-series feature extraction on large panel datasets.
    COPY-PASTE FIX
    functime is a powerful Python library for **embarrassingly parallel global forecasting** and **time-series feature extraction** on **large panel datasets**, built with **Polars** for unparalleled speed and scale.
  • mediumreadme#2
    Elevate the LLM agent feature in the README's Highlights

    Why:

    CURRENT
    ## Additional Highlights
`functime` comes with a specialized LLM agent to analyze, describe, and compare your forecasts. Check out the walkthrough here.
    COPY-PASTE FIX
    Add a new bullet point under 'Highlights':
- **LLM Agent:** Analyze, describe, and compare your forecasts with a specialized LLM agent.
  • lowtopics#3
    Add 'global-forecasting' to repository topics

    Why:

    CURRENT
    feature-engineering, forecasting, machine-learning, panel-data, polars, python, time-series
    COPY-PASTE FIX
    feature-engineering, forecasting, global-forecasting, machine-learning, panel-data, polars, python, time-series

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 functime-org/functime
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyCaret
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyCaret · recommended 1×
  2. Darts · recommended 1×
  3. Prophet · recommended 1×
  4. XGBoost · recommended 1×
  5. Featuretools · recommended 1×
  • CATEGORY QUERY
    How to perform scalable time-series forecasting and feature engineering on large panel datasets?
    you: not recommended
    AI recommended (in order):
    1. PyCaret
    2. Darts
    3. Prophet
    4. XGBoost
    5. Featuretools
    6. Databricks
    7. Apache Spark
    8. MLlib
    9. Spark SQL
    10. Google Cloud Platform (GCP)
    11. BigQuery ML
    12. Vertex AI
    13. BigQuery
    14. AWS
    15. Amazon Forecast
    16. Amazon SageMaker
    17. AWS Glue
    18. Amazon Athena
    19. Nixtla's StatsForecast
    20. Nixtla's HierarchicalForecast
    21. Nixtla's MLForecast
    22. Polars
    23. Pandas
    24. Ray
    25. Dask
    26. Modin
    27. TensorFlow
    28. PyTorch

    AI recommended 28 alternatives but never named functime-org/functime. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python library for embarrassingly parallel time-series feature extraction and forecasting using Polars?
    you: not recommended
    AI recommended (in order):
    1. tsfresh (blue-yonder/tsfresh)
    2. feature_engine (feature-engine/feature_engine)
    3. Pytorch Forecasting (pytorch-forecasting/pytorch-forecasting)
    4. sktime (sktime/sktime)
    5. Prophet (facebook/prophet)
    6. statsforecast (Nixtla/statsforecast)

    AI recommended 6 alternatives but never named functime-org/functime. 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 functime-org/functime?
    pass
    AI named functime-org/functime explicitly

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

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

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

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functime-org/functime — 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