STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems

• URL: http://arxiv.org/abs/2508.19011v2
• Date: Thu, 28 Aug 2025 09:55:10 GMT
• Title: STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
• Authors: Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic,
• Abstract summary: We propose STDiff, which reframes imputation as learning how the system evolves from one state to the next.<n> STDiff consistently achieves the lowest errors, with its advantage increasing for longer gaps.<n>These results support dynamics-aware, explicitly conditioned imputation as a robust approach for industrial time series.
• Score: 2.3895981099137535
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most deep learning methods for imputing missing values treat the task as completing patterns within a fixed time window. This assumption often fails in industrial systems, where dynamics are driven by control actions, are highly non-stationary, and can experience long, uninterrupted gaps. We propose STDiff, which reframes imputation as learning how the system evolves from one state to the next. STDiff uses a conditional denoising diffusion model with a causal bias aligned to control theory, generating missing values step-by-step based on the most recent known state and relevant control or environmental inputs. On a public wastewater treatment dataset with simulated missing blocks, STDiff consistently achieves the lowest errors, with its advantage increasing for longer gaps. On a raw industrial dataset with substantial real gaps, it produces trajectories that remain dynamically plausible, in contrast to window-based models that tend to flatten or over-smooth. These results support dynamics-aware, explicitly conditioned imputation as a robust approach for industrial time series, and we discuss computational trade-offs and extensions to broader domains.

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