skforecast/skforecast

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models

What it solves

Skforecast 简化了时间序列预测的过程，允许用户利用机器学习模型、统计方法和基础模型在一个统一的框架内进行。它弥补了标准 scikit-learn 兼容的估计器与时间序列数据的特定需求（例如滞后生成和递归预测策略）之间的差距。

How it works

该库提供 "Forecaster" 对象，作为训练和预测的容器。它支持多种策略，包括递归和直接预测，并可以处理单个或多个时间序列。它能与任何 scikit-learn 兼容的估计器（例如 LightGBM, XGBoost, 或 CatBoost）无缝集成，并也支持深度学习 (RNN/LSTM)、统计模型 (ARIMA, ETS) 以及基础模型。

Who it's for

它是为需要使用 Python 构建、验证和部署生产级时间序列预测模型的数据科学家和机器学习工程师而设计的。

Highlights

  • Scikit-learn Compatibility: Works with any estimator that follows the scikit-learn API.
  • Diverse Forecaster Types: Offers specialized forecasters for recursive, direct, multivariate, and classification tasks.
  • Comprehensive Tooling: Includes built-in tools for feature engineering, hyperparameter tuning, and backtesting for realistic performance evaluation.
  • Flexible Modalities: Supports exogenous features, window features, and probabilistic predictions.