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REPOGEO REPORT · LITE

moment-timeseries-foundation-model/moment

Default branch main · commit 38f7310a · scanned 6/9/2026, 6:03:32 PM

GitHub: 774 stars · 111 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)

3 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 moment-timeseries-foundation-model/moment, 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
    Strengthen README introduction to emphasize pre-trainable foundation model

    Why:

    CURRENT
    We introduce MOMENT, a family of open-source foundation models for general-purpose time-series analysis. Pre-training large models on time-series data is challenging due to (1) the absence a large and cohesive public time-series repository, and (2) diverse time-series
    COPY-PASTE FIX
    MOMENT is an open-source family of time-series foundation models designed to simplify and accelerate general-purpose time-series analysis. It enables researchers and data scientists to pre-train custom models on their own data and adapt them for tasks like forecasting, classification, and anomaly detection.
  • mediumtopics#2
    Add specific topics for pre-training and custom foundation models

    Why:

    CURRENT
    anomaly-detection, classification, forecasting, foundational-models, imputation, large-language-models, time-series, time-series-anomaly-detection, time-series-classification, time-series-forecasting, transformers
    COPY-PASTE FIX
    anomaly-detection, classification, forecasting, foundational-models, imputation, large-language-models, time-series, time-series-anomaly-detection, time-series-classification, time-series-forecasting, time-series-pretraining, custom-foundation-models, transformers
  • mediumabout#3
    Enhance repository description to highlight pre-trainable and general-purpose nature

    Why:

    CURRENT
    MOMENT: A Family of Open Time-series Foundation Models, ICML'24
    COPY-PASTE FIX
    MOMENT: A family of open-source, pre-trainable foundation models for general-purpose time-series analysis (forecasting, classification, anomaly detection, imputation). Accepted at ICML'24.

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 moment-timeseries-foundation-model/moment
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tensorflow/tensorflow
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 4×
  2. huggingface/transformers · recommended 2×
  3. DeepAR+ · recommended 1×
  4. Temporal Fusion Transformers · recommended 1×
  5. Informer / Autoformer · recommended 1×
  • CATEGORY QUERY
    Seeking a robust foundation model for various time series forecasting, classification, and anomaly detection tasks.
    you: not recommended
    AI recommended (in order):
    1. DeepAR+
    2. Temporal Fusion Transformers
    3. Informer / Autoformer
    4. Prophet
    5. XGBoost / LightGBM
    6. PyTorch Forecasting

    AI recommended 6 alternatives but never named moment-timeseries-foundation-model/moment. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I pre-train a custom time series foundation model using my own datasets?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Hugging Face Transformers (huggingface/transformers)
    3. Accelerate (huggingface/accelerate)
    4. Pandas (pandas-dev/pandas)
    5. NumPy (numpy/numpy)
    6. Hugging Face Trainer (huggingface/transformers)
    7. AWS
    8. Google Cloud
    9. Azure
    10. TensorFlow (tensorflow/tensorflow)
    11. Keras (keras-team/keras)
    12. TensorFlow Distributed (tensorflow/tensorflow)
    13. tf.data (tensorflow/tensorflow)
    14. tf.distribute.Strategy (tensorflow/tensorflow)
    15. JAX (google/jax)
    16. Flax (google/flax)
    17. Haiku (deepmind/dm-haiku)
    18. Google Cloud TPUs
    19. River (online-ml/river)
    20. GluonTS (awslabs/gluon-ts)
    21. Apache MXNet (apache/mxnet)

    AI recommended 21 alternatives but never named moment-timeseries-foundation-model/moment. 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 moment-timeseries-foundation-model/moment?
    pass
    AI named moment-timeseries-foundation-model/moment explicitly

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

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

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

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moment-timeseries-foundation-model/moment — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite