Probabilistic time series forecasts with autoregressive transformation models

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 by various research directions to unite expressive distributional forecasts using a semi-parametric distribution assumption with an interpretable model specification. We demonstrate the properties of ATMs both theoretically and through empirical evaluation on several simulated and real-world forecasting datasets.

Automated summary

Probabilistic forecasting of time series is a crucial task in various applications and research domains. This paper introduces Autoregressive Transformation Models (ATMs), which aim to combine expressive distributional forecasts with an interpretable model specification by utilizing a semi-parametric distribution assumption. The properties of ATMs are demonstrated through both theoretical analysis and empirical evaluation on simulated and real-world forecasting datasets.