TY - RPRT U1 - Arbeitspapier A1 - Chandrasekaran, Sowmya A1 - Zaefferer, Martin A1 - Moritz, Steffen A1 - Stork, Jörg A1 - Friese, Martina A1 - Fischbach, Andreas A1 - Bartz-Beielstein, Thomas T1 - Data Preprocessing: A New Algorithm for Univariate Imputation Designed Specifically for Industrial Needs N2 - Data pre-processing is a key research topic in data mining because it plays a crucial role in improving the accuracy of any data mining algorithm. In most real world cases, a significant amount of the recorded data is found missing due to most diverse errors. This loss of data is nearly always unavoidable. Recovery of missing data plays a vital role in avoiding inaccurate data mining decisions. Most multivariate imputation methods are not compatible to univariate datasets and the traditional univariate imputation techniques become highly biased as the missing data gap increases. With the current technological advancements abundant data is being captured every second. Hence, we intend to develop a new algorithm that enables maximum utilization of the available big datasets for imputation. In this paper, we present a Seasonal and Trend decomposition using Loess (STL) based Seasonal Moving Window Algorithm, which is capable of handling patterns with trend as well as cyclic characteristics. We show that the algorithm is highly suitable for pre-processing of large datasets. T3 - CIplus - 7/2016 KW - Time Series KW - Imputation KW - Univariate Data Y1 - 2016 U6 - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-4331 UN - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-4331 SP - 24 S1 - 24 ER -