63 citations · 104 across the 4 of their papers we have counts for
5 papers
The Nixtlaverse: An Open-Source Ecosystem for Forecasting
Olivier Sprangers, Max Mergenthaler Canseco, Marco Peixeiro +8
Large forecasting applications often combine statistical, machine-learning, and neural models. These families solve the same problem but differ in fitted state, training procedures…
Hierarchical Forecasting at Scale
Olivier Sprangers, Wander Wadman, Sebastian Schelter +1
Existing hierarchical forecasting techniques scale poorly when the number of time series increases. We propose to learn a coherent forecast for millions of time series with a singl…
Parameter Efficient Deep Probabilistic Forecasting
Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
Probabilistic time series forecasting is crucial in many application domains such as retail, ecommerce, finance, or biology. With the increasing availability of large volumes of da…
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression
Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
Gradient Boosting Machines (GBM) are hugely popular for solving tabular data problems. However, practitioners are not only interested in point predictions, but also in probabilisti…
Reinforcement learning for port-Hamiltonian systems
Olivier Sprangers, Gabriel A. D. Lopes, Robert Babuska
Passivity-based control (PBC) for port-Hamiltonian systems provides an intuitive way of achieving stabilization by rendering a system passive with respect to a desired storage func…