Examples and Tutorials

Practical examples and tutorials to help you understand and apply skforecast.

English

Skforecast: time series forecasting with machine learning

ARIMA and SARIMAX models

Forecasting with gradient boosting: XGBoost, LightGBM and CatBoost

Forecasting with XGBoost

Global Forecasting Models: Multi-series forecasting

Probabilistic forecasting

Forecasting with Deep Learning

Stacking ensemble of machine learning models to improve forecasting

Modelling time series trend with tree based models

Interpretable forecasting models

Forecasting time series with missing values

Intermittent demand forecasting

Forecasting energy demand with machine learning

Global Forecasting Models: Comparative Analysis of Single and Multi-Series Forecasting Modeling

Mitigating the impact of covid on forecasting models

Forecasting web traffic with machine learning and Python

Bitcoin price prediction with Python

Español

Skforecast: forecasting series temporales con machine learning

Modelos ARIMA y SARIMAX

Forecasting con gradient boosting: XGBoost, LightGBM y CatBoost

Forecasting con XGBoost

Global Forecasting Models: Multi-series forecasting

Modelos de forecasting globales: Análisis comparativo de modelos de una y múltiples series

Forecasting probabilístico

Forecasting con Deep Learning

Modelar series temporales con tendencia utilizando modelos de árboles

Interpretabilidad en modelos de forecasting

Forecasting de series incompletas con valores faltantes

Predicción demanda intermitente

Forecasting de la demanda eléctrica

Forecasting de las visitas a una página web

Reducir el impacto del Covid en modelos de forecasting

Predicción del precio de Bitcoin con Python

Workshop predicción de series temporales con machine learning Universidad de Deusto / Deustuko Unibertsitatea