2015 IEEE Conference on Visual Analytics Science and Technology (VAST) (2015)
Chicago, IL, USA
Oct. 25, 2015 to Oct. 30, 2015
M. Bogl , Vienna University of Technology, Austria
P. Filzmoser , Vienna University of Technology, Austria
T. Gschwandtner , Vienna University of Technology, Austria
S. Miksch , Vienna University of Technology, Austria
W. Aigner , St. Pölten University of Applied Sciences, Germany
A. Rind , St. Pölten University of Applied Sciences, Germany
T. Lammarsch , MODUL University Vienna, Austria
Missing values are a problem in many real world applications, for example failing sensor measurements. For further analysis these missing values need to be imputed. Thus, imputation of such missing values is important in a wide range of applications. We propose a visually and statistically guided imputation approach, that allows applying different imputation techniques to estimate the missing values as well as evaluating and fine tuning the imputation by visual guidance. In our approach we include additional visual information about uncertainty and employ the cyclic structure of time inherent in the data. Including this cyclic structure enables visually judging the adequateness of the estimated values with respect to the uncertainty/error boundaries and according to the patterns of the neighbouring time points in linear and cyclic (e.g., the months of the year) time.
M. Bogl et al., "Visually and statistically guided imputation of missing values in univariate seasonal time series," 2015 IEEE Conference on Visual Analytics Science and Technology (VAST), Chicago, IL, USA, 2015, pp. 189-190.