Self-attention-based Diffusion Model for Time-series Imputation in Partial Blackout Scenarios
This addresses data imputation for time series in scenarios like sensor malfunctions, which is important for improving machine learning performance in fields like IoT or healthcare, but it is incremental as it extends existing methods to a more general missing pattern.
The paper tackles the problem of missing values in multivariate time series data under partial blackout scenarios, where a subset of features is missing for consecutive time steps, by introducing a two-stage imputation process using self-attention and diffusion processes, and demonstrates that it outperforms state-of-the-art methods in experiments on benchmark and real-world datasets.
Missing values in multivariate time series data can harm machine learning performance and introduce bias. These gaps arise from sensor malfunctions, blackouts, and human error and are typically addressed by data imputation. Previous work has tackled the imputation of missing data in random, complete blackouts and forecasting scenarios. The current paper addresses a more general missing pattern, which we call "partial blackout," where a subset of features is missing for consecutive time steps. We introduce a two-stage imputation process using self-attention and diffusion processes to model feature and temporal correlations. Notably, our model effectively handles missing data during training, enhancing adaptability and ensuring reliable imputation and performance, even with incomplete datasets. Our experiments on benchmark and two real-world time series datasets demonstrate that our model outperforms the state-of-the-art in partial blackout scenarios and shows better scalability.