mlsanityData to 7 Oct 2026

Probabilistic Time Series Forecasts with Autoregressive Transformation Models

arXiv 2110.08248 ↗First posted 15 Oct 2021cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 24 of its 69 references that are in the map. First posted on arXiv 15 Oct 2021.

Builds on

24 of 69 references.

References · 24

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12 more
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Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 0; 2024: 1; 2025: 1.

7 papers.

  1. 2022A review of predictive uncertainty estimation with machine learningcited by 29
  2. 2022Distributional Gradient Boosting Machinescited by 5
  3. 2022Deep interpretable ensemblescited by 5
  4. 2022Bernstein Flows for Flexible Posteriors in Variational Bayescited by 3
  5. 2024How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regressioncited by 2
  6. 2022Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecastscited by 0
  7. 2025Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginalscited by 0