STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
This addresses the challenge of accurate imputation in industrial systems for applications like monitoring and control, though it is incremental as it builds on diffusion models with domain-specific adaptations.
The paper tackled the problem of imputing missing values in industrial time series, where existing methods fail due to non-stationary dynamics and long gaps, by proposing STDiff, a diffusion framework that learns state transitions based on control inputs, achieving the lowest errors on a wastewater dataset with simulated gaps and producing dynamically plausible trajectories on a raw industrial dataset.
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.