Nixtla/nixtla
TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀. observed · 2026-08-28
Health v2 · maintenance only
97/100
- Activity 99
- Release rhythm 94
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 29.0
- age_days: 1805
- days_rel: 43
- days_push: 7
- n_releases_24m: 13
Adoption not part of the score
3996 stars · 332 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Nixtla's TimeGPT is a production-ready pre-trained foundation model for time series forecasting and anomaly detection, accessed via a Python SDK and hosted API (with Azure and self-hosted enterprise options). It performs zero-shot inference and fine-tuning across domains like retail, electricity, finance, and IoT.
Use cases
- forecast future values of a time series with a few lines of code
- detect anomalies in historical or streaming time series data
- zero-shot forecasting without training a model per series
- fine-tune a pretrained forecasting model on my own dataset
- compare against ARIMA, LightGBM, or deep learning forecasters
- real-time online anomaly monitoring for metrics
- forecast retail sales, electricity demand, or financial data
When to choose
- you need accurate forecasts quickly without per-series model training or tuning
- you want a hosted or self-hosted foundation model for time series with a simple Python API
- you need both forecasting and anomaly detection from one service
- you lack the compute or expertise to train deep learning forecasters
When to avoid
- you need fully open-source weights or offline inference without an enterprise agreement
- you require strict data residency but cannot afford self-hosted deployment
- you need classical statistical forecasting with full interpretability
- your data is not time series (TimeGPT is not an LLM for text)
Facets
library · maturity active
machine-learning deep-learning sdk http-client data-science time-series artificial-intelligence data-science analytics python cloud self-hosted timegpt foundation-model forecasting anomaly-detection zero-shot-inference api-client time-series-foundation-model
10 sources
- readme: https://github.com/Nixtla/nixtla · fetched 2026-08-28 · 153ed5c535dc
- homepage: https://www.nixtla.io/docs · fetched 2026-08-29 · 171c4f3a1bf3
- site_page: https://www.nixtla.io/docs/introduction/introduction · fetched 2026-08-29 · 171c4f3a1bf3
- site_page: https://www.nixtla.io/docs/introduction/why_timegpt · fetched 2026-08-29 · af9ac7479ad2
- site_page: https://www.nixtla.io/docs/introduction/about_timegpt · fetched 2026-08-29 · 8857f955f446
- site_page: https://www.nixtla.io/docs/introduction/timegpt_subscription_plans · fetched 2026-08-29 · 997ad84ff499
- site_page: https://www.nixtla.io/docs/introduction/faq · fetched 2026-08-29 · b511d25a5175
- site_page: https://www.nixtla.io/docs/setup/setting_up_your_api_key · fetched 2026-08-29 · c6773c313ea2
- site_page: https://www.nixtla.io/docs/setup/python_wheel · fetched 2026-08-29 · c54ddff6d359
- registry_pypi: https://pypi.org/pypi/nixtla/json · fetched 2026-08-29 · b82849ef23db
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Nixtla/nixtla | main | 97 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem