RRepoGEO

REPOGEO REPORT · LITE

blue-yonder/tsfresh

Default branch main · commit 69e50a56 · scanned 5/18/2026, 3:16:47 AM

GitHub: 9,215 stars · 1,266 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
91 /100
Healthy
Category recall
2 / 2
Avg rank #1.5 when recommended
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 blue-yonder/tsfresh, 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 to clarify specific niche

    Why:

    CURRENT
    This repository contains the *TSFRESH* python package. The abbreviation stands for
*"Time Series Feature extraction based on scalable hypothesis tests"*.
    COPY-PASTE FIX
    This repository contains the *TSFRESH* python package. The abbreviation stands for
*"Time Series Feature extraction based on scalable hypothesis tests"*. It focuses exclusively on automating the generation of descriptive features from time series, rather than forecasting or general machine learning tasks.
  • mediumcomparison#2
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., '## Comparison with other tools' or '## When to use tsfresh'. This section should briefly explain how `tsfresh` differs from tools like `Featuretools` (general vs. time series specific), `Prophet` (feature extraction vs. forecasting), and `Sktime` (focused feature extraction vs. broader time series ML framework).
  • lowtopics#3
    Refine topics for greater specificity

    Why:

    CURRENT
    data-science, feature-extraction, time-series
    COPY-PASTE FIX
    data-science, feature-extraction, time-series, time-series-feature-engineering, automated-feature-engineering

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
2 / 2
100% of queries surface blue-yonder/tsfresh
Avg rank
#1.5
Lower is better. #1 = top recommendation.
Share of voice
15%
Of all named tools, what % are you?
Top rival
Featuretools
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Featuretools · recommended 1×
  2. Facebook Prophet · recommended 1×
  3. Sktime · recommended 1×
  4. Kats · recommended 1×
  5. PyCaret · recommended 1×
  • CATEGORY QUERY
    How can I automate feature engineering for time series data analysis?
    you: #2
    AI recommended (in order):
    1. Featuretools
    2. tsfresh ← you
    3. Facebook Prophet
    4. Sktime
    5. Kats
    6. PyCaret
    7. TPOT
    Show full AI answer
  • CATEGORY QUERY
    Python package for systematic feature extraction and selection from time series?
    you: #1
    AI recommended (in order):
    1. tsfresh (tsfresh/tsfresh) ← you
    2. feature_engine (feature-engine/feature-engine)
    3. sktime (sktime/sktime)
    4. Pandas (pandas-dev/pandas)
    5. scikit-learn (scikit-learn/scikit-learn)
    6. PyCaret (pycaret/pycaret)
    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 blue-yonder/tsfresh?
    pass
    AI named blue-yonder/tsfresh explicitly

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

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

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

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blue-yonder/tsfresh — 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