36 citations · 80 across the 13 of their papers we have counts for
4 papers · 1 filter
Probabilistic Time Series Forecasts with Autoregressive Transformation Models
David Rügamer, Philipp F. M. Baumann, Thomas Kneib +1
Probabilistic forecasting of time series is an important matter in many applications and research fields. In order to draw conclusions from a probabilistic forecast, we must ensure…
Joint Debiased Representation Learning and Imbalanced Data Clustering
Mina Rezaei, Emilio Dorigatti, David Ruegamer +1
One of the most promising approaches for unsupervised learning is combining deep representation learning and deep clustering. Some recent works propose to simultaneously learn repr…
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
David Rügamer, Chris Kolb, Cornelius Fritz +11
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of…
Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
Cornelius Fritz, Emilio Dorigatti, David Rügamer
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease…