skforecast
Skforecast is a Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models. It works with any estimator compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.
Decision gist · record as of 2026-08-14
Yes. Skforecast is actively maintained, has low install friction, carries a permissive BSD-3-Clause license, and has no known vulnerabilities. It solves a genuine problem—using scikit-learn models for time series forecasting—with a mature API and strong community backing. Install it if you need to forecast time series with scikit-learn compatible estimators.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Requires a scikit-learn compatible estimator to be provided.
- Low install friction with a pure Python wheel and nine well-established runtime dependencies (numpy, pandas, scikit-learn, scipy, optuna, joblib, numba, tqdm, rich).
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause permissive license allows commercial and private use with minimal restrictions, making it suitable for both open-source and proprietary projects.
last release 2026-07-08 (37 days) · last repo commit 2026-08-14 · 1,524 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 126,956 downloads/mo, #11,757 on PyPI
Alternatives
Verify before relying
pip install skforecast
from skforecast.recursive import ForecasterRecursive
from skforecast.datasets import load_demo_dataset
import pandas as pd
y = load_demo_dataset()
forecaster = ForecasterRecursive(
estimator=None,
lags=15
)
forecaster.fit(y=y)
predictions = forecaster.predict(steps=12)- Which specific scikit-learn compatible estimators are recommended or tested with skforecast.
- Performance benchmarks comparing skforecast to alternative time series libraries.
- Specific memory or computational requirements for large-scale forecasting tasks.
- Whether the library supports GPU acceleration for model training and prediction.
What it is and what it does
Skforecast bridges scikit-learn and time series forecasting by letting you use any scikit-learn compatible regressor to forecast future values. It handles the complexity of converting time series data into supervised learning problems through lagged features and provides both single-series and multi-series workflows. The library includes tools for feature engineering, model selection, hyperparameter tuning via optuna, and backtesting to validate forecasts on historical data before deployment.
The package is built on numpy, pandas, and scikit-learn, with optional acceleration via numba and parallel job execution through joblib. It targets developers and data scientists who want production-ready forecasting without building custom pipelines, and it integrates naturally into existing scikit-learn workflows. The library is actively maintained, supports Python 3.10 through 3.14, and is NumFOCUS affiliated.
Use it for
- Build multi-step ahead forecasts for time series data using scikit-learn compatible regressors.
- Perform hyperparameter tuning and model selection for forecasting tasks using optuna integration.
- Backtest forecasting models on historical data to evaluate realistic performance before deployment.
- Forecast multiple related time series simultaneously with flexible single and multi-series workflows.
- Engineer lagged features and other time series transformations automatically for supervised learning.
- Integrate forecasting into production pipelines using scikit-learn compatible estimators.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Skforecast is actively maintained, has low install friction, carries a permissive BSD-3-Clause license, and has no known vulnerabilities. It solves a genuine problem—using scikit-learn models for time series forecasting—with a mature API and strong community backing. Install it if you need to forecast time series with scikit-learn compatible estimators.
Install
skforecast on PyPI
Before you install
Low install friction with a pure Python wheel and nine well-established runtime dependencies (numpy, pandas, scikit-learn, scipy, optuna, joblib, numba, tqdm, rich). Actively maintained with recent releases and strong community engagement.
Requires Python 3.10 or later. Requires a scikit-learn compatible estimator to be provided.
License in practice
BSD-3-Clause permissive license allows commercial and private use with minimal restrictions, making it suitable for both open-source and proprietary projects.
Quickstart
pip install skforecast
from skforecast.recursive import ForecasterRecursive
from skforecast.datasets import load_demo_dataset
import pandas as pd
y = load_demo_dataset()
forecaster = ForecasterRecursive(
estimator=None,
lags=15
)
forecaster.fit(y=y)
predictions = forecaster.predict(steps=12)Verify before relying
- Which specific scikit-learn compatible estimators are recommended or tested with skforecast.
- Performance benchmarks comparing skforecast to alternative time series libraries.
- Specific memory or computational requirements for large-scale forecasting tasks.
- Whether the library supports GPU acceleration for model training and prediction.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesnumpypandastqdmscikit-learnscipyoptunajoblibnumbarich |
| Maintenance | Actively maintained 37 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 126,956 / month, #11,757 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development |
Evidence: skforecast-0.23.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “scikit-learn forecasting”
- skforecastSkforecast is a Python library for time series forecasting that works…
- sktimesktime provides a unified interface for time series machine learning…
- pmdarimaPmdarima provides automated ARIMA time series forecasting with a…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also pycaret · sktime · feature-engine · ifcopenshell · statsforecast · neuralforecast · pykalman · scikit-base · mlforecast · sklearn-crfsuite