no code implementations • 15 Oct 2021 • David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn
Probabilistic forecasting of time series is an important matter in many applications and research fields.
1 code implementation • 15 Feb 2021 • Max Horn, Kumar Shridhar, Elrich Groenewald, Philipp F. M. Baumann
While Transformer architectures have show remarkable success, they are bound to the computation of all pairwise interactions of input element and thus suffer from limited scalability.
no code implementations • 15 Oct 2020 • Philipp F. M. Baumann, Torsten Hothorn, David Rügamer
Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings.
no code implementations • 4 Mar 2020 • Philipp F. M. Baumann, Michael Schomaker, Enzo Rossi
The estimation procedure incorporates machine learning algorithms and is tailored to address the challenges associated with complex longitudinal macroeconomic data.