ISSN 1991-2927
 

ACP № 3 (65) 2021

Author: "Denis Valerevich Zavarzin"

Tatiana Vasilevna Afanaseva, Ulyanovsk State Technical University, Doctor of Engineering, Associate Professor, Deputy Head of the Department of Information Systems of Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering of UlSTU; an author of articles and monographs in the field of fuzzy modeling and time series analysis. [e-mail: tv.afanasjeva@gmail.com]T. Afanaseva,

Ivan Valerevich Sibirev, State Technical University, Postgraduate Student at the Department of Information Systems of UlSTU; graduated from the Faculty of Economics and Mathematics of UlSTU; an author of articles in the field of fuzzy techniques in multivariate statistical analysis. [e-mail: ivan.sibirev@yandex.ru]I. Sibirev,

Denis Valerevich Zavarzin, Ulyanovsk State Technical University, Postgraduate student at the Department of Information Systems of UlSTU; graduated from the Faculty of Information Systems and Technologies of UlSTU; an author of articles in the field of fuzzy modeling and time series anomaly analysis. [e-mail: dzavarzin@gmail.com]D. Zavarzin

Application of Fuzzy Models in the Analysis of Processes in Organizational-technical Systems 51_11.pdf

The article discusses the application of the Fuzzy models for time series analysis. The main aspect of the analysis is the formation of groups of similar processes. For the grouping of processes, they are considered in the form of discrete time series. Traditional methods for clustering time series based on point-by-point clustering, on attributes or models, do not take into account the nature of the behavior of the time series, which ensures the information content and quality of clustering. To solve this problem, a clustering algorithm based on a fuzzy approach, called Fuzzy Behavior Clustering (FBC approach), was proposed. The proposed time series clustering approach considers the behavior of time series at three levels of the hierarchy (at the level of the general tendency, the level of the parameters and at the level of values). The behavior of time series at the level of the main tendency is described by means of linguistic terms. At the heart of the FBC-approach lie a fuzzy representation of time series and an algorithm for a linguistic assessment of the main tendency. The application of the FBC-approach for analyzing and clustering time series of KPI dynamics of IT project developers based on data from the repository is provided.

Fuzzy behavior clustering, fuzzy clustering behavior, combined clustering of time series, the linguistic terms, extraction of fuzzy tendencies of time series.

2018_ 1

Sections: Computer-aided engineering

Subjects: Computer-aided engineering.


Tatiana Vasilevna Afanaseva, Ulyanovsk State Technical University, Doctor of Engineering; Associate Professor, Deputy Head of Information Systems Department at Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University; an author of articles and monographs in the field of the intellectual analysis of time series. [e-mail: tv.afanasjeva@gmail.com]T. Afanaseva,

Aleksei Andreevich Sapunkov, Ulyanovsk State Technical University, Postgraduate Student at the Information Systems Department of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of the intellectual analysis of time series. [e-mail: sapalks@gmail.com]A. Sapunkov,

Denis Valerevich Zavarzin, Ulyanovsk State Technical University, Postgraduate Student at the Information Systems Department of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of the intellectual analysis of time series. [e-mail: dzavarzin91@gmail.com]D. Zavarzin

Using the K-means Clustering Algorithm for Improving the Temporal Statitics of Commercial Proposals Views 000_6.pdf

Anomalies are considered as not typical and rare values, which decrease accuracy in date significant. Such values would generally cause inaccuracy in date analysis results, so they must be deleted. The article proposes to use the k-means clustering method in order to solve practical problems of data processing for displaying the temporal statistics in the B2B sector. The B2BFamily service for sending and tracking commercial offer represents the subject area and the data source. The article also proposes to remove anomalies and display more adequate temporal view statistics about the average time of the commercial offer slide review. That will help the sales manager to adjust the strategy of communication with customers. Finally, the authors discuss the results and trends of the study further development.

B2bfamily, clustering, anomaly, b2bfamily, k-means clustering algorithm, detection and removal of anomalies.

2016_ 4

Sections: Mathematical modeling

Subjects: Mathematical modeling.


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