skforecast
Python library for time series forecasting with machine learning models.
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—Overview
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.”
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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.
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Get Started with skforecast
Step-by-step setup guide with code examples and common gotchas.