# Probabilistic time series forecasts with autoregressive transformation models

@article{Rgamer2021ProbabilisticTS, title={Probabilistic time series forecasts with autoregressive transformation models}, author={David R{\"u}gamer and Philipp F. M. Baumann and Thomas Kneib and Torsten Hothorn}, journal={Statistics and Computing}, year={2021}, volume={33} }

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 that the model class used to approximate the true forecasting distribution is expressive enough. Yet, characteristics of the model itself, such as its uncertainty or its feature-outcome relationship are not of lesser importance. This paper proposes Autoregressive Transformation Models (ATMs), a model class inspired…

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