Technologies for processing and cleaning data, detecting and eliminating noise in the time series
The efficiency of information processing is primarily due to the quality of the input data massive. Input data cleaning is an important stage of any analysis. The presence of noise and anomalies can significantly affect the result and lead to false conclusion. As well as excessive cleaning accompanied by the loss of potentially valuable surveillance elements. Despite the constant optimization of data collection and processing systems, the development of an effective mistakes eliminating methodology is still an area of increased interest for scientific community. The continuously increasing volume of information flows determines the need to develop an adequate tool for time series cleaning. The task of noise identification accuracy increasing becomes extremely urgent nowadays. The study provides an overview of existing noise identifying and eliminating technics on one-dimensional and multidimensional time-series used in domestic and foreign practice, their features and disadvantages are emphasized. Russian and foreign approaches to technologies classification are considered and analyzed. As the result of analysis, a set of the most effective technics was determined.