This thesis describes performance measures and ensemble architectures for deterministic and probabilistic forecasts using the application example of wind power forecasting and proposes a novel scheme for the situation-dependent aggregation ...

This book constitutes the refereed proceedings of the 5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016, held in Madrid, Spain, in April 2016.

Deep probabilistic forecasting is gaining attention across numerous applications.

This thesis improves oil- and gas-well profitability by quantifying the uncertainty of the production-forecasting process, using probabilistic machine learning (ML) techniques.

This book brings together all of the important new results on the state space framework for exponential smoothing.

This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications.

Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.

This book is a must-read for students, industry professionals, and anyone interested in forecasting for smart control applications, demand-side response, energy markets, and renewable utilization.

The author considers the problem of sequential probability forecasting in the most general setting, where the observed data may exhibit an arbitrary form of stochastic dependence.