Diffusion-based Time Series Imputation and Forecasting with Structured
State Space Models
- URL: http://arxiv.org/abs/2208.09399v3
- Date: Sat, 6 May 2023 12:43:37 GMT
- Title: Diffusion-based Time Series Imputation and Forecasting with Structured
State Space Models
- Authors: Juan Miguel Lopez Alcaraz and Nils Strodthoff
- Abstract summary: We put forward SSSD, an imputation model that relies on two emerging technologies,conditional diffusion models and structured state space models.
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.
- Score: 2.299617836036273
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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-of-the-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.
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