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Recurrence Quantification Analysis Technique for Detecting an Extremal Events

Student: Zaspenko Vladislavs

Supervisor: Andrey Dmitriev

Faculty: Graduate School of Business

Educational Programme: Business Informatics (Bachelor)

Final Grade: 7

Year of Graduation: 2019

The study of complex systems, whether they are natural or artificial, provide a large amount of information on the basis of which one can describe and understand the nature of the processes in order to use the knowledge gained in forecasting for some time period ahead. From this, the main goal of such studies is formed - the construction of mathematical models that would reflect as closely as possible real processes and could be used to solve existing problems of early detection and prediction of extreme events. In this thesis work will be considered information about the ideas and theory of quantitative recurrent analysis in the detection of extreme events. Among other things, the applied part of the work is devoted to the application of quantitative recurrent analysis for processing the data of the microblogging social network Twitter, as well as the interpretation of recurrent diagrams based on simulated data.

Full text (added May 14, 2019)

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