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Probabilistic forecasting of time series is an important matter in manyapplications and research fields. In order to draw conclusions from aprobabilistic forecast, we must ensure that the model class used to approximatethe true forecasting distribution is expressive enough. Yet, characteristics ofthe model itself, such as its uncertainty or its feature-outcome relationshipare not of lesser importance. This paper proposes Autoregressive TransformationModels (ATMs), a model class inspired by various research directions to uniteexpressive distributional forecasts using a semi-parametric distributionassumption with an interpretable model specification. We demonstrate theproperties of ATMs both theoretically and through empirical evaluation onseveral simulated and real-world forecasting datasets.

Probabilistic Time Series Forecasts with Autoregressive Transformation Models