skforecast

What it is

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.

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.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited documentation and examples

   The official documentation lacks comprehensive tutorials and practical use cases, making it difficult for new users to get started.

2. 02

   Small community and limited support

   There are few active contributors and a small user base, leading to fewer bug fixes, feature requests, and community-driven improvements.

3. 03

   Performance issues with large datasets

   Skforecast can be slow when processing large time series datasets due to the underlying machine learning models' computational requirements.

What it does well

• Supports any regressor compatible with the scikit-learn API.

• Integrates well with popular machine learning models like LightGBM, XGBoost, and CatBoost.

• Facilitates time series forecasting for various applications.

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.