Impute With Confidence: A Framework for Uncertainty Aware Multivariate Time Series Imputation
This addresses the need for reliable imputation in healthcare data with missing values, though it is incremental as it builds on existing methods by adding uncertainty estimation.
The paper tackles the problem of missing values in multivariate time series data, particularly in healthcare, by introducing a framework that quantifies uncertainty for selective imputation, resulting in reduced imputation errors and improved performance in a 24-hour mortality prediction task.
Time series data with missing values is common across many domains. Healthcare presents special challenges due to prolonged periods of sensor disconnection. In such cases, having a confidence measure for imputed values is critical. Most existing methods either overlook model uncertainty or lack mechanisms to estimate it. To address this gap, we introduce a general framework that quantifies and leverages uncertainty for selective imputation. By focusing on values the model is most confident in, highly unreliable imputations are avoided. Our experiments on multiple EHR datasets, covering diverse types of missingness, demonstrate that selectively imputing less-uncertain values not only reduces imputation errors but also improves downstream tasks. Specifically, we show performance gains in a 24-hour mortality prediction task, underscoring the practical benefit of incorporating uncertainty into time series imputation.