Перегляд за автором "Kirichenko, L."
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Публікація Analysis of the properties of ordinary levy motion based on the estimation of stability index(Sofia : ITHEA, 2014) Kirichenko, L.; Shergin, V.The work proposes a method for estimating the stability index of alpha-stable distributions by using moments of fractional order. Provided numerical modeling has fully justified all of the results. Comparative analysis of the efficiency among the proposed method of estimating the stability index and widely used methods was performed. Proposal method is much simpler, far more faster and substantially less memory required. Estimation of generalized Hurst exponent from time series of the ordinary Lévy process was performed. Multifractal fluctuation analysis method and evaluation based on stability index estimation were compared. The results of numerical modelling showed that proposed method for estimating the fractal properties of the ordinary Lévy process, based on stability index estimation via fractional order moments is a much more accurate.Публікація Analyzes of the Distributed System Load with Multifractal Input Data Flows(2017) Radivilova, T.; Kirichenko, L.The paper proposes a solution an actual scientific problem related to load balancing and efficient utilization of resources of the distributed system. The proposed method is based on calculation of load CPU, memory, and bandwidth by flows of different classes of service for each server and the entire distributed system and taking into account multifractal properties of input data flows. Weighting factors were introduced that allow to determine the significance of the characteristics of server relative to each other. Thus, this method allows to calculate the imbalance of the all system servers and system utilization. The simulation of the proposed method for different multifractal parameters of input flows was conducted. The simulation showed that the characteristics of multifractal traffic have a appreciable effect on the system imbalance. The usage of proposed method allows to distribute requests across the servers thus that the deviation of the load servers from the average value was minimal, that allows to get a higher metrics of system performance and faster processing flows.Публікація Comparative analysis of machine learning classification оf time series with fractal properties(2019) Radivilova, T.; Kirichenko, L.; Bulakh, V.The article analyses the classification of time series according to their fractal properties by machine learning. The classification was carried out using neural networks and the random forest method. Objects were the model fractal time series with given the Hurst exponent. Each class was a set of time series with the Hurst exponent values in a predetermined range. Input features were the values of time series. It was demonstrated that in this case the classification accuracy is high enough. The most accurate classification results were obtained using recurrent neural network. The proposed method can be readily used in practice for recognition, classification and clustering of time series with fractal properties.Публікація Detecting cyber threats through social network analysis: short survey(SocioEconomic Challenges, Volume 1, Issue 1, 2017, 2017.) Kirichenko, L.; Radivilova, T.; Carlsson, A.This article considers a short survey of basic methods of social networks analysis, which are used for detecting cyber threats. The main types of social network threats are presented. Basic methods of graph theory and data mining, that deals with social networks analysis are described. Typical security tasks of social network analysis, such as community detection in network, detection of leaders in communities, detection experts in networks, clustering text information and others are considered.Публікація Dynamic load balancing algorithm of distributed systems(2016) Ivanisenko, I.; Kirichenko, L.; Radivilova, T.The dynamic load balancing algorithm based on the monitoring server load, self-similar characteristics of passing traffic have to provide a statistically uniform load distribution on servers, high performance, fault tolerance and capacity, low response time, the amount of overhead and losses was propose in work. Integrated measurement for the total imbalance level of the system were entered.Публікація Forecasting Weakly Correlated Time Series in Tasks of Electronic Commerce(2017) Kirichenko, L.; Radivilova, T.; Zinkevich, I.Forecasting of weakly correlated time series of conversion rate by methods of exponential smoothing, neural network and decision tree on the example of conversion percent series for an electronic store is considered in the paper. The advantages and disadvantages of each method are considered.Публікація Fractal time series analysis of social network activities(2017) Kirichenko, L.; Radivilova, T.; Bulakh, V.In the work, a comparative correlation and fractal analysis of time series of Bitcoin crypto currency rate and community activities in social networks associated with Bitcoin was conducted. A significant correlation between the Bitcoin rate and the community activities was detected. Time series fractal analysis indicated the presence of self-similar and multifractal properties. The results of researches showed that the series having a strong correlation dependence have a similar multifractal structure.Публікація Generalized Approach to Analysis of Multifractal Properties from Short Time Series(ХНУРЕ, 2020) Kirichenko, L.; Abed Saif Ahmed, Alghawli; Radivilova, T.The paper considers a generalized approach to the time series multifractal analysis. The focus of research is on the correct estimation of multifractal characteristics from short time series. Based on numerical modeling and estimating, the main disadvantages and advantages of the sample fractal characteristics obtained by three methods: the multifractal fluctuation detrended analysis, wavelet transform modulus maxima and multifractal analysis using discrete wavelet transform are studied. The generalized Hurst exponent was chosen as the basic characteristic for comparing the accuracy of the methods. A test statistic for determining the monofractal properties of a time series using the multifractal fluctuation detrended analysis is proposed. A generalized approach to estimating the multifractal characteristics of short time series is developed and practical recommendations for its implementation are proposed. A significant part of the study is devoted to practical applications of fractal analysis. The proposed approach is illustrated by the examples of multifractal analysis of various real fractal time series.Публікація Generalized approach to Hurst exponent estimating by time series(2018) Kirichenko, L.; Radivilova, T.; Bulakh, V.This paper presents a generalized approach to the fractal analysis of self-similar random processes by short time series. Several stages of the fractal analysis are proposed. Preliminary time series analysis includes the removal of short-term dependence, the identification of true long-term dependence and hypothesis test on the existence of a self-similarity property. Methods of unbiased interval estimation of the Hurst exponent in cases of stationary and non-stationary time series are discussed. Methods of estimate refinement are proposed. This approach is applicable to the study of self-similar time series of different nature.Публікація Investigation of multifractal properties of additive data stream(Publishing House of Lviv Polytechnic National University, 2016) Radivilova, T.; Ivanisenko, I.; Kirichenko, L.The work presents results of a numerical study of fractal characteristics of multifractal stream at addition of stream, which does not have multifractal properties. They showed that the generalized Hurst exponent of total stream tends to one of original multifractal stream with increase in signal/noise ratio.Публікація Investigation of Self-similar Properties of Additive Data Traffic(Lviv, 2015) Radivilova, T.; Ivanisenko, I.; Kirichenko, L.Публікація Investigation of Time Series of Original Values of Currency Rates Measured on Small Time Frames on FOREX Using Methods of Chaos Theory(ХНУРЭ, 2009) Dabi-Prash, O.; Kirichenko, L.Lately, the linear paradigm with its idea of normal distribution of profits has been replaced with the nonlinear approach and Chaos Theory which gives the explanation of the complex behavior of financial markets. It has been discovered that time series of profits measured on long time frames on currency and stock markets (time series of monthly prices etc.) are chaotic. This paper is concentrated on investigation of time series of original values of currency rates measured on short time frames on FOREX (hourly, 4-hourly, daily prices) using methods of Chaos Theory (Time-delay reconstruction method, Grassberger-Procaccia method, estimation of the Lyapunov exponent) in order to define if such time series are chaotic as well.Публікація Machine Learning Classification of Multifractional Brownian Motion Realizations(ХНУРЕ, 2020) Kirichenko, L.; Radivilova, T.; Bulakh, V.A comparative analysis of machine learning classification of stochastic time series based on their multifractal properties is proposed. Multifractal time series were obtained by generating realizations of fractional Brownian motion in multifractal time. The features for classification were statistical, fractal and recurrent characteristics calculated for each time series. The various machine learning classifiers were chosen for classification: bagging with classification and regression decision trees, random forest with classification and regression decision trees, fully connected perceptron and recurrent neural network. Both cumulative time series of multifractal Brownian motion and time series increments were carried out. It was shown that in general, classification accuracy is higher when using series of increments. When classifying realizations of multifractional Brownian motion, bagging and recurrent neural network showed the best accuracy.Публікація Mathematical simulation of self-similar network traffic with aimed parameters("Tibiscus" University of Timişoara, Romania, 2013) Kirichenko, L.; Radivilova, T.The objective of the given work is to study the queues resulting in the buffer while self-similar traffic passing through a network nod and to build a mathematical model of an actual traffic. The latest researches of different types of network traffics bring out clearly that network traffic is defined by self- similarity and long-term dependence. The self-similar traffic has specific structure being reserved at various measures - its realization is characterized by some vast emissions when respecting low medium-scale traffic. This fact degrades the performance significantly (increases, losses and delays) while running through the network nods. Hence, it follows that commonly used methods of simulation and network system calculations rested on traditional assumptions do not reflect the real situation taking place in the network.Публікація The method of distinction monofractal and multifractal process from time series(2014) Kirichenko, L.Based on the numerical analysis of the sample multifractal characteristics obtained by method of multifractal detrended fluctuation analysis the statistical criterion for accepting the hypothesis of monofractal properties of the time series is proposed. Results of investigations to identify mono- and multifractal properties of the time series of different nature are given.