Comparative analysis of anomaly recognition methods in real time

The article discusses modern classes of algorithms used to detect anomalies in data streams: slidingwindow algorithm, metric algorithms, predictive-based algorithms, and algorithms based on hiddenMarkov models. During the research, it was possible to determine functional and efficiency criteriafor assessing the class of algorithms and then comparing it with other considered classes. In addition,for each class of methods, strengths and weaknesses are given, the scope is described, and a generalizedexample of implementation in the form of pseudo code is given. The use of this approach makesit possible to cover entire groups of algorithms without reference to a specific implementation. Theconclusions obtained as a result of the research can be applied solving problems of optimizing theprocess of detecting anomalies or increasing the efficiency of applied solutions used in these scenarios.The resulting calculations allow further development and optimization of methods in this areafor unlabeled fixed data sets.