Examples and Tutorials¶
Practical examples and tutorials to help you understand and apply skforecast.
Getting Started: Fundamental Forecasting¶
This section provides essential tutorials for users who are just getting started with time series forecasting. These examples cover the most fundamental models and techniques to help you build a strong foundation in forecasting.
Skforecast: time series forecasting with machine learning
Forecasting with gradient boosting: XGBoost, LightGBM and CatBoost
Global Models: Multi-Series Forecasting¶
These tutorials focus on global models and multi-series forecasting, where you can explore the use of techniques that handle multiple time series simultaneously and compare performance across different forecasting approaches.
Global Forecasting Models: Multi-series forecasting
Scalable Forecasting: Modeling thousand time series with a single global model
Global Forecasting Models: Comparative Analysis of Single and Multi-Series Forecasting Modeling
Forecasting with Deep Learning
Advanced Techniques: Beyond Basic Models¶
For experienced users looking to deepen their forecasting skills, this section provides advanced techniques, including probabilistic forecasting, handling missing values, and more sophisticated ensemble methods.
Modelling time series trend with tree based models
Forecasting time series with missing values
Interpretable forecasting models
Stacking ensemble of machine learning models to improve forecasting
Real-World Challenges and Case Studies¶
This section includes real-world applications of time series forecasting to tackle specific challenges, such as forecasting energy demand, web traffic, and even cryptocurrency prices. Learn how to apply forecasting techniques to practical use cases.
Forecasting energy demand with machine learning
Forecasting web traffic with machine learning and Python
Intermittent demand forecasting