Lopez Alcaraz, Juan Miguel and Strodthoff, Nils (2023) Diffusion-based time series imputation and forecasting with structured atate apace models. Transactions on machine learning research. pp. 1-36. ISSN 2835-8856

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Abstract

The imputation of missing values represents a significant obstacle for many real-world data analysis pipelines. Here, we focus on time series data and put forward SSSD, an imputation model that relies on two emerging technologies, (conditional) diffusion models as state-ofthe-art generative models and structured state space models as internal model architecture, which are particularly suited to capture long-term dependencies in time series data. We demonstrate that SSSD matches or even exceeds state-of-the-art probabilistic imputation and forecasting performance on a broad range of data sets and different missingness scenarios, including the challenging blackout-missing scenarios, where prior approaches failed to provide meaningful results.

Item Type:	Article
Divisions:	Faculty of Medicine and Health Sciences > Department of Public Health and Medical Education
Date Deposited:	02 Mar 2023 10:49
Last Modified:	02 Mar 2023 10:49
URI:	https://oops.uni-oldenburg.de/id/eprint/5697
URN:	urn:nbn:de:gbv:715-oops-57786
DOI:
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