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time series forecasting

MOMENT-1-small

MOMENT-1-small is an open-source time-series-forecasting model available on HuggingFace. Details are sourced from the public model registry.

Last reviewed

Use cases

  • Building time-series-forecasting applications
  • Research and experimentation
  • Open-source AI prototyping

Pros

  • Open weights available
  • Community support on HuggingFace

Cons

  • Requires manual evaluation for production use
  • Licensing terms vary — check model card

When does MOMENT-1-small fit?

Picking a time series forecasting model means matching MOMENT-1-small's declared task to your specific input distribution. Public benchmarks rarely predict downstream behaviour, so treat MOMENT-1-small's reported numbers as a starting point, not a verdict.

  • You're picking a time series forecasting model for production → MOMENT-1-small is a candidate, but always validate against your own evaluation set before committing — public benchmarks rarely predict downstream task performance.

Real-world usage signals

6 likes is on the quiet side. MOMENT-1-small may be too new for community signal, or it may be filling a very specific niche that doesn't generate public reactions.

17 tags — MOMENT-1-small is positioned for a specific bundle of related tasks. Likely a strong fit for the named use cases and weaker outside them.

Publisher information is incomplete on the model card. Cross-reference MOMENT-1-small against the GitHub repo or paper before treating provenance as established.

How we look at time series forecasting models

MOMENT-1-small has crossed the threshold from "experiment" to "actively-used" on HuggingFace. The community has enough hands-on experience that you can find real deployment reports, but not so much that MOMENT-1-small is a default choice in this category.

Download count alone is a thin signal — it conflates "people trying it" with "people running it in production." For MOMENT-1-small specifically: 509,421 downloads — solid usage, but you may need to read source code rather than tutorials when something goes wrong. Pair that with the engagement read above, the date of the most recent issue activity, and a 30-minute trial run on your own evaluation set before deciding whether MOMENT-1-small earns a place in your stack.

Frequently asked questions

Can I use MOMENT-1-small commercially?

mit is a permissive license, so commercial use including modification and distribution is allowed. Read the actual license text on the model card to confirm — license tags can be misapplied.

Is MOMENT-1-small actively maintained?

509,421 downloads — solid usage, but you may need to read source code rather than tutorials when something goes wrong.

What should I check before depending on MOMENT-1-small in production?

Three things: (1) the license text — assume nothing from the tag alone; (2) the most recent issues on the HuggingFace repo to gauge how the maintainers respond to bug reports; (3) reproducibility — run the model card's stated benchmark on your own hardware and confirm the numbers match within 1-2%. Discrepancies usually mean different precision or a tokenizer version mismatch.

Tags

transformerspytorchsafetensorstime seriesforecastingclassificationanomaly detectionimputationpretrained modelsfoundation modelstime-seriestime-series-forecastingdataset:AutonLab/Timeseries-PILEarxiv:2402.03885license:mitendpoints_compatibleregion:us