Nixtla/statsforecast
Lightning ⚡️ fast forecasting with statistical and econometric models.
Quickly and accurately generate future predictions from your historical business data. This tool takes in your time-stamped operational metrics (like sales, inventory levels, or website traffic) and outputs reliable forecasts along with confidence intervals. It's designed for data analysts, business intelligence professionals, and operations managers who need to make data-driven decisions based on future trends.
4,718 stars. Used by 5 other packages. Actively maintained with 7 commits in the last 30 days. Available on PyPI.
Use this if you need to forecast a large number of time series datasets quickly and with high accuracy, perhaps for inventory planning, demand forecasting, or resource allocation.
Not ideal if your forecasting needs are primarily for qualitative future trend analysis rather than precise quantitative predictions from historical data.
Stars
4,718
Forks
360
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 12, 2026
Commits (30d)
7
Dependencies
12
Reverse dependents
5
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Nixtla/statsforecast"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related frameworks
sktime/sktime
A unified framework for machine learning with time series
aeon-toolkit/aeon
A toolkit for time series machine learning and deep learning
Nixtla/neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
Nixtla/mlforecast
Scalable machine 🤖 learning for time series forecasting.