statsforecast
Time series forecasting suite using statistical models
Decision gist · record as of 2026-08-14
Yes. StatsForecast is actively maintained, has no known vulnerabilities, and offers a mature, well-documented suite of statistical forecasting models with strong performance characteristics. The permissive Apache license and broad Python version support (3.10–3.14) make it low-risk. Install friction is moderate due to multiple dependencies, but pre-built wheels and active maintenance mitigate this. Suitable for production forecasting and benchmarking work.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; depends on compiled dependencies (numpy, scipy, statsmodels).
- Medium install friction due to 10 runtime dependencies including numpy, pandas, scipy, and statsmodels.
- Package is actively maintained with recent release (29 days old) and broad Python version support (3.10–3.14).
License · maintenance · safety
Apache Software License 2.0 (permissive) — Licensed under Apache Software License 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions.
last release 2026-07-16 (29 days) · last repo commit 2026-08-12 · 4,866 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,895,194 downloads/mo, #3,454 on PyPI
Alternatives
Verify before relying
pip install statsforecast
from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA
df = AirPassengersDF
sf = StatsForecast(models=[AutoARIMA(season_length=12)], freq='ME')
sf.fit(df)
sf.predict(h=12, level=[95])- Whether distributed execution (Spark, Dask, Ray) requires additional setup beyond the base install.
- Performance benchmarks cited in description (e.g., '20x faster than pmdarima') and their applicability to typical use cases.
- Specific memory or computational requirements for fitting millions of series as claimed.
What it is and what it does
StatsForecast is a Python library for univariate time series forecasting built around statistical and econometric models. It provides automatic model selection and fitting for ARIMA, ETS, CES, Theta, and related methods, optimized for speed and accuracy. The library includes both point forecasts and probabilistic forecasts with confidence intervals, supports exogenous variables and static covariates, and integrates with distributed computing frameworks (Spark, Dask, Ray) for scaling to large numbers of time series.
The package is designed for production forecasting and benchmarking scenarios where speed and accuracy matter. It uses sklearn-like syntax (fit/predict) and includes utilities for anomaly detection, cross-validation, and handling multiple seasonalities. Dependencies include numpy, pandas, scipy, and statsmodels for core statistical computation, plus utilities for parallel processing and data handling.
Use it for
- Fit automatic ARIMA or ETS models to hundreds of thousands of time series in parallel using Ray or Dask.
- Generate probabilistic forecasts with confidence intervals for demand planning or inventory management.
- Detect anomalies in time series using in-sample prediction intervals.
- Forecast electricity load or other multi-seasonal data using MSTL decomposition.
- Replace or benchmark against other forecasting libraries (e.g., Prophet) with faster, more accurate models.
- Handle intermittent demand or sparse time series data with specialized models.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
StatsForecast is actively maintained, has no known vulnerabilities, and offers a mature, well-documented suite of statistical forecasting models with strong performance characteristics. The permissive Apache license and broad Python version support (3.10–3.14) make it low-risk. Install friction is moderate due to multiple dependencies, but pre-built wheels and active maintenance mitigate this. Suitable for production forecasting and benchmarking work.
Install
statsforecast on PyPI
Before you install
Medium install friction due to 10 runtime dependencies including numpy, pandas, scipy, and statsmodels. Package is actively maintained with recent release (29 days old) and broad Python version support (3.10–3.14). Pre-built wheels available for multiple platforms.
Requires Python 3.10 or later; depends on compiled dependencies (numpy, scipy, statsmodels).
License in practice
Licensed under Apache Software License 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions.
Quickstart
pip install statsforecast
from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA
df = AirPassengersDF
sf = StatsForecast(models=[AutoARIMA(season_length=12)], freq='ME')
sf.fit(df)
sf.predict(h=12, level=[95])Verify before relying
- Whether distributed execution (Spark, Dask, Ray) requires additional setup beyond the base install.
- Performance benchmarks cited in description (e.g., '20x faster than pmdarima') and their applicability to typical use cases.
- Specific memory or computational requirements for fitting millions of series as claimed.
Package facts
| License | Apache Software License 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 10 packagescloudpicklecoreforecastnumpypandasscipystatsmodelstqdmfugueutilsforecastthreadpoolctl |
| Maintenance | Actively maintained 29 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,895,194 / month, #3,454 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: statsforecast-2.1.1-cp310-cp310-macosx_10_9_x86_64.whl; statsforecast-2.1.1-cp310-cp310-macosx_11_0_arm64.whl; statsforecast-2.1.1-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; statsforecast-2.1.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; statsforecast-2.1.1-cp310-cp310-win_amd64.whl; statsforecast-2.1.1-cp311-cp311-macosx_10_9_x86_64.whl; statsforecast-2.1.1-cp311-cp311-macosx_11_0_arm64.whl; statsforecast-2.1.1-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; statsforecast-2.1.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; statsforecast-2.1.1-cp311-cp311-win_amd64.whl; statsforecast-2.1.1-cp312-cp312-macosx_10_13_x86_64.whl; statsforecast-2.1.1-cp312-cp312-macosx_11_0_arm64.whl; statsforecast-2.1.1-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; statsforecast-2.1.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; statsforecast-2.1.1-cp312-cp312-win_amd64.whl; statsforecast-2.1.1-cp313-cp313-macosx_10_13_x86_64.whl; statsforecast-2.1.1-cp313-cp313-macosx_11_0_arm64.whl; statsforecast-2.1.1-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; statsforecast-2.1.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; statsforecast-2.1.1-cp313-cp313-win_amd64.whl
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