Learn¶

forecasting example using xgb, gru, cnn, lstm stc (notebook)¶

https://github.com/ritikdhame/Electricity_Demand_and_Price_forecasting

many forecast examples¶

https://cienciadedatos.net/documentos/py39-forecasting-time-series-with-skforecast-xgboost-lightgbm-catboost

probabilistic forecasting¶

• Bootstrapping: https://skforecast.org/latest/user_guides/probabilistic-forecasting-bootstrapped-residuals.html

• Conformal prediction: https://skforecast.org/latest/user_guides/probabilistic-forecasting-conformal-prediction.html

• Quantile regression: https://skforecast.org/latest/user_guides/probabilistic-forecasting-quantile-regression.html

kaggle¶

https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/472793

lagged and rolling features (notebook):

• https://www.kaggle.com/code/ymatioun/enefit-ym1

Forecasting: Principles and Practice¶

https://otexts.com/fpp3/index.html

https://github.com/zgana/fpp3-python-readalong

Python-centered read-along of the excellent Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos

https://github.com/Nixtla¶

A collection of libraries for time series forecasting, optimized for speed and developer experience.

https://github.com/cuge1995/awesome-time-series¶

A list of papers, datasets, Kaggle competitions for various tasks in time series.

books¶

• python machine learning: https://github.com/rasbt/python-machine-learning-book-3rd-edition

• demand forecast best practices

• Introduction to Time Series Analysis and Forecasting, 2nd Edition