User Guides Table of Contents

Welcome to the skforecast user guides! This comprehensive collection of guides is designed to help you navigate through the various features and functionalities of skforecast. Whether you are a beginner or an advanced user, you will find the necessary resources to master time series forecasting with skforecast. Below, you will find the user guides categorized by topic for easier navigation.

Single series Forecasters

• Recursive multi-step forecasting
• Direct multi-step forecasting
• Forecasting with custom predictors
• ARIMA and SARIMAX forecasting
• Foreasting baseline

Global Forecasters (multiple series)

• Independent multi-time series forecasting
• Series with different lengths and different exogenous variables
• Dependent multivariate series forecasting
• Deep learning Recurrent Neural Networks

Feature Engineering

• Exogenous variables
• Feature selection
• Categorical features
• Calendars features

Model Evaluation and Tuning

• Backtesting forecaster
• Hyperparameter tuning and lags selection

Probabilistic Forecasting

• Probabilistic forecasting

Advanced Topics

• Scikit-learn Transformers and Pipelines
• Forecaster in production
• Save and load forecaster
• Explainability
• Forecasting with XGBoost and LightGBM
• Skforecast in GPU
• Plot forecaster residuals

Additional Resources

• Input data
• Datasets
• FAQ and forecasting tips

We hope you find these guides helpful. If you have any questions or need further assistance, please don't hesitate to reach out to the skforecast community. Remember to visit the FAQ and forecasting tips page for answers to frequently asked questions and forecasting tips.