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M. Bögl, W. Aigner, P. Filzmoser, T. Gschwandtner, T. Lammarsch, S. Miksch, A. Rind:
"Visually and Statistically Guided Imputation of Missing Values in Univariate Seasonal Time Series";
Poster: IEEE Vis 2015, Chicago, IL, USA; 2015-10-25 - 2015-10-30; in: "Poster Proceedings of the IEEE Visualization Conference 2015", J. Yang, E. Bertini, N. Elmqvist, T. Dwyer, X. Yuan, H. Carr (ed.); (2015), 2 pages.

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.

Visual Analytics, Time Series, Imputation, Missing Values

http://publik.tuwien.ac.at/files/PubDat_242014.pdf

Project Head Silvia Miksch:
CVAST: Centre for Visual Analytics Science and Technology (Laura Bassi Centre of Expertise)