@techreport{FrieseStorkRamosGuerraetal.2013, author = {Friese, Martina and Stork, J{\"o}rg and Ramos Guerra, Ricardo and Bartz-Beielstein, Thomas and Thaker, Soham and Flasch, Oliver and Zaefferer, Martin}, title = {UniFIeD Univariate Frequency-based Imputation for Time Series Data}, isbn = {2194-2870}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos-493}, year = {2013}, abstract = {This paper introduces UniFIeD, a new data preprocessing method for time series. UniFIeD can cope with large intervals of missing data. A scalable test function generator, which allows the simulation of time series with different gap sizes, is presented additionally. An experimental study demonstrates that (i) UniFIeD shows a significant better performance than simple imputation methods and (ii) UniFIeD is able to handle situations, where advanced imputation methods fail. The results are independent from the underlying error measurements.}, subject = {Zeitreihe}, language = {en} }