Visual Imputation Analytics for Missing Time-Series Data in Bayesian Network

Bayesian network is derived from conditional probability and is useful in inferring the next state of the currently observed variables. If data are missed or corrupted during data collection or transfer, the characteristics of the original data may be distorted and biased. Therefore, predicted values from the Bayesian network designed with incomplete data are not reliable. Various techniques have been studied to resolve the imperfection in data using statistical techniques or machine learning, but since the complete data is unknown, there is no optimal way to impute missing values. In this paper, we present a visual analytics system that supports decision-making to impute missing values occurring in incomplete time series data. The visual analytics system allows data analysts to explore the cause of missing data in incomplete datasets. The system also enables us to compare the performance of a suitable imputation model with the Bayesian network accuracy and the Kolmogorov-Smirnov test. We evaluate how the visual analytics system supports the decision-making process for the data imputation through a use case.