RRepoGEO

REPOGEO REPORT · LITE

lmmentel/awesome-time-series

Default branch main · commit 2e10c56d · scanned 6/10/2026, 1:57:42 PM

GitHub: 692 stars · 98 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
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 lmmentel/awesome-time-series, 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
  • highabout#1
    Clarify the repository description to emphasize its 'awesome list' nature

    Why:

    CURRENT
    Resources for working with time series and sequence data
    COPY-PASTE FIX
    A curated list of awesome resources for working with time series and sequence data, covering libraries, tools, papers, and courses.
  • highlicense#2
    Add a LICENSE file and clarify licensing in the README

    Why:

    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT or Apache-2.0) to clarify usage rights for the awesome list itself. Also, add a section to the README clarifying that the licenses of the *listed* resources vary and should be checked individually.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Set the 'Homepage' URL in the repository settings to `https://github.com/lmmentel/awesome-time-series` (or a dedicated GitHub Pages site if one exists/is created).

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 lmmentel/awesome-time-series
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. statsmodels · recommended 1×
  3. PyFlux · recommended 1×
  4. scikit-learn · recommended 1×
  5. tsfresh · recommended 1×
  • CATEGORY QUERY
    What are the best Python libraries for time series forecasting and anomaly detection?
    you: not recommended
    AI recommended (in order):
    1. Prophet
    2. statsmodels
    3. PyFlux
    4. scikit-learn
    5. tsfresh
    6. Pmdarima
    7. ruptures

    AI recommended 7 alternatives but never named lmmentel/awesome-time-series. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find comprehensive resources for sequential data analysis and time series data mining?
    you: not recommended
    AI recommended (in order):
    1. Forecasting: Principles and Practice
    2. Time Series Analysis and Its Applications: With R Examples
    3. Coursera Specializations
    4. Practical Time Series Analysis
    5. Time Series Analysis
    6. Kaggle Learn (Time Series Micro-Course)
    7. Applied Time Series Analysis with R
    8. The Elements of Statistical Learning
    9. Deep Learning for Time Series Forecasting

    AI recommended 9 alternatives but never named lmmentel/awesome-time-series. 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 lmmentel/awesome-time-series?
    pass
    AI named lmmentel/awesome-time-series explicitly

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

  • If a team adopts lmmentel/awesome-time-series in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lmmentel/awesome-time-series 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 lmmentel/awesome-time-series solve, and who is the primary audience?
    pass
    AI did not name lmmentel/awesome-time-series — 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?

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lmmentel/awesome-time-series — 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