The paper deals with computation techniques applied in preprocessing of gravity data, which are based on time series analysis by using mathematical and statistical smoothing techniques such as moving average, moving median, cumulative and moving average, etc. The main aim of gravity data preprocessing is to avoid abrupt errors caused by a sudden movement of the subsoil due to human or natural activities or systematic instrumental influences and so provide relevant gravity values, which are then subjected to further processing. The new approach of the described research involves the preprocessing phase in gravity data analysis to identify and avoid gross errors, which could influence the size of unknown parameters estimated by the least square method in the processing phase.