Visually and Statistically Guided Imputation of Missing Values in Univariate Seasonal Time Series

TitleVisually and Statistically Guided Imputation of Missing Values in Univariate Seasonal Time Series
Publication TypeConference Paper
Year of Publication2015
AuthorsBögl, M., W. Aigner, P. Filzmoser, T. Gschwandtner, T. Lammarsch, S. Miksch, and A. Rind
EditorsYang, J., E. Bertini, N. Elmqvist, T. Dwyer, X. Yuan, and H. Carr
Conference NamePoster Proceedings of the IEEE Visualization Conference 2015
Conference LocationChicago, USA
KeywordsImputation, Missing Values, time series, Visual analytics
Abstract

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.

Notes

Best Poster Award

URLhttp://publik.tuwien.ac.at/files/PubDat_242014.pdf
AttachmentSize
Poster3.33 MB
Best Poster Award599.84 KB
Funding projects: 
CVAST
Funding projects: 
HypoVis