@techreport{FrieseStorkRamosGuerraetal.2013, author = {Martina Friese and J{\"o}rg Stork and Ricardo Ramos Guerra and Thomas Bartz-Beielstein and Soham Thaker and Oliver Flasch and Martin Zaefferer}, title = {UniFIeD Univariate Frequency-based Imputation for Time Series Data}, isbn = {2194-2870}, url = {https://nbn-resolving.org/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.}, language = {en} }