Modeling uncertainty with engression: a deep generative time-series approach

Modeling uncertainty with engression: a deep generative time-series approach | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 May 2025 V1 Latest version Share on Modeling uncertainty with engression: a deep generative time-series approach Authors : Basil Kraft 0000-0002-8491-2730 [email protected] , Steven Stalder 0009-0000-4568-8652 , William Hugo Aeberhard , Nicolás Harrington Ruiz , Nicolai Meinshausen , Xinwei Shen , and Lukas Gudmundsson 0000-0003-3539-8621 Authors Info & Affiliations https://doi.org/10.22541/au.174837849.96948239/v1 Published Geophysical Research Letters Version of record Peer review timeline 424 views 343 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Deep learning enables precise environmental predictions and simulations across spatial and temporal scales. However, reliable uncertainty estimation with generative capabilities remains crucial for actionable forecasting and simulation, yet robust and simple quantification methods remain challenging. Recently, engression, a generative approach for model-agnostic training and uncertainty quantification, has been proposed. We evaluate its feasibility for environmental time-series modeling, specifically applying it to rainfall-runoff prediction using long short-term memory (LSTM) networks. As a benchmark, we use quantile regression, a generalization of the mean absolute error (MAE). Our results show that engression-LSTM is an effective and easy-to-use method for generative modeling, outperforming quantile regression in uncertainty quantification. Furthermore, qualitative analysis of the generated runoff time series indicates high dynamic fidelity. These findings highlight engression-LSTM as a promising approach for incorporating uncertainty into environmental time-series modeling. Supplementary Material File (1033628_0_merged_1747130854.pdf) Download 890.67 KB File (engression.pdf) Download 890.67 KB Information & Authors Information Version history V1 Version 1 27 May 2025 Peer review timeline Published Geophysical Research Letters Version of Record 24 Jan 2026 Published Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords engression generative modeling hydrology long short-term memory (lstm) rainfall-runoff prediction time-series modeling uncertainty quantification Authors Affiliations Basil Kraft 0000-0002-8491-2730 [email protected] Eidgenossische Technische Hochschule Zurich Departement Umweltsystemwissenschaften View all articles by this author Steven Stalder 0009-0000-4568-8652 ETH Zürich - Swiss Data Science Center View all articles by this author William Hugo Aeberhard Swiss Data Science Center View all articles by this author Nicolás Harrington Ruiz Eidgenossische Technische Hochschule Zurich Departement Umweltsystemwissenschaften View all articles by this author Nicolai Meinshausen ETH Zurich View all articles by this author Xinwei Shen Eidgenossische Technische Hochschule Zurich Departement Mathematik View all articles by this author Lukas Gudmundsson 0000-0003-3539-8621 ETH Zürich View all articles by this author Funding Information European Commission ‭101137682‬ MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV Swiss Data Science Center C20-02 Metrics & Citations Metrics Article Usage 424 views 343 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Basil Kraft, Steven Stalder, William Hugo Aeberhard, et al. Modeling uncertainty with engression: a deep generative time-series approach. Authorea . 27 May 2025. DOI: https://doi.org/10.22541/au.174837849.96948239/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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