@inproceedings{bogl_visually_2015, title = {Visually and {Statistically} {Guided} {Imputation} of {Missing} {Values} in {Univariate} {Seasonal} {Time} {Series}}, url = {http://publik.tuwien.ac.at/files/PubDat_242014.pdf}, doi = {10/gh3744}, 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.}, urldate = {2015-11-19}, booktitle = {Poster {Proceedings} of the {IEEE} {Visualization} {Conference} 2015}, author = {Bögl, Markus and Filzmoser, Peter and Gschwandtner, Theresia and Miksch, Silvia and Aigner, Wolfgang and Rind, Alexander and Lammarsch, Tim}, year = {2015}, note = {Projekt: KAVA-Time}, keywords = {2015, Department Medien und Digitale Technologien, Department Technologie, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Publikationstyp Schriftpublikation, Statistical Analysis, Time-Oriented Data, Visual analytics, missing values, peer-reviewed, time-series, visualization}, }