THE METHOD OF ADAPTATION OF THE PARAMETERS OF ALGORITHMS FOR THE DETECTION AND CLEANING OF A STATISTICAL SAMPLE FROM ANOMALIES FOR DATA SCIENCE PROBLEMS

Context. Popularization of the Data Science for the tasks of e-commerce, the banking sector of the economy, for the tasks of managing dynamic objects – all this actualizes the requirements for indicators of the efficiency of data processing in the Time Series format. This also applies to the preparatory stage of data analysis at the level of detection and cleaning of statistical samples from anomalies such as rough measurements and omissions.Objective. The development of the method for adapting the parameters of the algorithms for detecting and cleaning the statistical sample of the Time Series format from anomalies for Data Science problems.Method. The article proposes a method for adapting the parameters of algorithms for detecting and cleaning a statistical sample from anomalies for data science problems. The proposed approach is based on and differs from similar practices by the introduction of an optimization approach in minimizing the dynamic and statistical error of the model, which determines the parameters of settings of popular algorithms for cleaning the statistical sample from anomalies using the Moving Window Method.Result. The introduction of the proposed approach into the practice of Data Science allows the development of software components for cleaning data from anomalies, which are trained by parameters purely according to the structure and dynamics of the Time Series.Conclusions. The key advantage of the proposed method is its simple implementation into existing algorithms for clearing the sample from anomalies and the absence of the need for the developer to select parameters for the settings of the cleaning algorithms manually, which saves time during development. The effectiveness of the proposed method is confirmed by the results of calculations