TECHNOLOGIES OF DATA PROCESSING AND CLEANING, NOISE IDENTIFICATION AND REMOVAL AT TIME SERIES

The efficiency of analytical information processing is primarily due to the quality of the input data array. Cleaning the input data is an important step in any analysis. The presence of noise and anomalies can significantly affect the result of the study and lead to an erroneous conclusion. However, as well as excessive cleaning, accompanied by the loss of potentially valuable elements of observation. Despite the constant optimization of systems for collecting and processing information, the development of an effective methodology for eliminating inaccuracies in a data set is still an area of increased interest in the scientific community. The continuously increasing volume of information flows predetermines the need to form an adequate tool for cleaning time series from noise. Especially relevant in modern conditions is the task of improving the accuracy of identifying noise elements.
The article provides an overview of existing methods for identifying and eliminating the noise component in one-dimensional and multidimensional time series, used in foreign practice, their features and shortcomings are emphasized. Foreign approaches to the classification of technologies are considered and analyzed. Based on the results of the analysis, a set of the most effective techniques was determined. 
Keywords: time series, data cleaning, noise, filtering and noise removal, array of data.