skforecast
Python library for time series forecasting with machine learning models.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is skforecast?
Skforecast is a Python library designed to facilitate time series forecasting using machine learning regressors compatible with the scikit-learn API. It supports various popular models like LightGBM, XGBoost, and CatBoost, making it versatile for different forecasting needs.
Key differentiator
“Skforecast stands out by offering a flexible and extensible framework that leverages existing scikit-learn compatible models to perform time series forecasting, making it an ideal choice for developers who want to quickly integrate advanced forecasting capabilities into their projects.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The official documentation lacks comprehensive tutorials and practical use cases, making it difficult for new users to get started.
There are few active contributors and a small user base, leading to fewer bug fixes, feature requests, and community-driven improvements.
Skforecast can be slow when processing large time series datasets due to the underlying machine learning models' computational requirements.
Fit analysis
Who is it for?
✓ Best for
Data scientists who need to integrate machine learning models into their time series forecasting workflows.
Developers working on projects that require accurate predictions based on historical data.
✕ Not a fit for
Projects requiring real-time or near-real-time forecasting capabilities, as skforecast is designed for batch processing.
Applications where the primary focus is not on machine learning-based forecasting but on other types of analysis.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Next step
Get Started with skforecast
Step-by-step setup guide with code examples and common gotchas.