Gorshenin Andrey Konstantinovich

Publications in Math-Net.Ru

1. Toward clustering of network computing infrastructure objects based on analysis of statistical anomalies in network traffic
   
   Inform. Primen., 17:3 (2023),  76–87
2. Increasing FOREX trading profitability with LSTM candlestick pattern recognition and tick volume indicator
   
   Inform. Primen., 16:3 (2022),  26–38
3. Method for improving accuracy of neural network forecasts based on probability mixture models and its implementation as a digital service
   
   Inform. Primen., 15:3 (2021),  63–74
4. On deep Gaussian mixture models in machine learning problems
   
   Intelligent systems. Theory and applications, 25:4 (2021),  121–124
5. On modeling trading strategies for currency pairs using deep neural networks and method of moving separation of mixtures
   
   Intelligent systems. Theory and applications, 25:4 (2021),  92–95
6. Statistical estimation of distributions of random coefficients in the Langevin stochastic differential equation
   
   Inform. Primen., 14:3 (2020),  3–12
7. Approximation of particle size distributions of lunar regolith based on the resampling
   
   Inform. Primen., 14:2 (2020),  50–57
8. Analysis of configurations of LSTM networks for medium-term vector forecasting
   
   Inform. Primen., 14:1 (2020),  10–16
9. Application of recurrent neural networks to forecasting the moments of finite normal mixtures
   
   Inform. Primen., 13:3 (2019),  114–121
10. Hybrid extreme gradient boosting models to impute the missing data in precipitation records
    
    Inform. Primen., 13:3 (2019),  34–40
11. Optimization of hyperparameters of neural networks using high-performance computing for prediction of precipitation
    
    Inform. Primen., 13:1 (2019),  75–81
12. Development of services of digital platforms to overcome nonfinancial barriers
    
    Inform. Primen., 12:4 (2018),  106–112
13. New mixture representations of the generalized Mittag-Leffler distribution and their applications
    
    Inform. Primen., 12:4 (2018),  75–85
14. Determining the extremes of precipitation volumes based on the modified “Peaks over Threshold” method
    
    Inform. Primen., 12:4 (2018),  16–24
15. Data noising by finite normal and gamma mixtures with application to the problem of rounded observations
    
    Inform. Primen., 12:3 (2018),  28–34
16. Forecasting moments of finite normal mixtures using feedforward neural networks
    
    Sistemy i Sredstva Inform., 28:3 (2018),  62–71
17. Pattern-based analysis of probabilistic and statistical characteristics of extreme precipitation
    
    Inform. Primen., 11:4 (2017),  38–46
18. On some mathematical and programming methods for construction of structural models of information flows
    
    Inform. Primen., 11:1 (2017),  58–68
19. Learning management system ELIS. User interface and functional capabilities
    
    Sistemy i Sredstva Inform., 27:2 (2017),  70–84
20. Learning management system ELIS. Architecture solutions
    
    Sistemy i Sredstva Inform., 27:2 (2017),  60–69
21. MSM Tools as a heterogeneous computing service
    
    Sistemy i Sredstva Inform., 27:1 (2017),  60–72
22. Analytical solution of the optimal control task of a semi-Markov process with finite set of states
    
    Inform. Primen., 10:4 (2016),  72–88
23. Development of the algorithm of numerical solution of the optimal investment control problem in the closed dynamical model of three-sector economy
    
    Inform. Primen., 10:1 (2016),  82–95
24. Concept of online service for stochastic modeling of real processes
    
    Inform. Primen., 10:1 (2016),  72–81
25. Application of the CUDA architecture for implementation of grid-based algorithms for the method of moving separation of mixtures
    
    Sistemy i Sredstva Inform., 26:4 (2016),  60–73
26. On a realization of an automated testing service
    
    Sistemy i Sredstva Inform., 26:1 (2016),  62–75
27. Statistical modeling of air–sea turbulent heat fluxes by the method of moving separation of finite normal mixtures
    
    Inform. Primen., 9:4 (2015),  3–13
28. A visualization of estimators in the method of moving separation of mixtures
    
    Inform. Primen., 8:4 (2014),  78–84
29. Information technology to research the fine structure of chaotic processes in plasma by the analysis of spectra
    
    Sistemy i Sredstva Inform., 24:1 (2014),  116–127
30. Parallelism in microprocessors
    
    Sistemy i Sredstva Inform., 24:1 (2014),  46–60
31. Probability and statistical modeling of information flows in complex financial systems based on high-frequency data
    
    Inform. Primen., 7:1 (2013),  12–21
32. On the investigation of plasma turbulence by the analysis of the spectra
    
    Computer Research and Modeling, 4:4 (2012),  793–802
33. On application of the asymptotic tests for estimating the number of mixture distribution components
    
    Computer Research and Modeling, 4:1 (2012),  45–53
34. On stability of normal location mixtures with respect to variations in mixing distribution
    
    Inform. Primen., 6:2 (2012),  22–28
35. Stability of normal scale mixtures with respect to variations in mixing distribution
    
    Sistemy i Sredstva Inform., 22:1 (2012),  167–179
36. An asymptotically optimal test for the number of components of a mixture of probability distributions
    
    Inform. Primen., 5:3 (2011),  4–16
37. The evolution of probability characteristics of low-frequency plasma turbulence
    
    Matem. Mod., 23:5 (2011),  35–55
38. Analysis of fine stochastic structure of chaotic processes by kernel estimators
    
    Matem. Mod., 23:4 (2011),  83–89
39. On convergence of SEM estimates sequence for statistical separation of mixture distribution
    
    Vestnik TVGU. Ser. Prikl. Matem. [Herald of Tver State University. Ser. Appl. Math.], 2011, no. 23,  39–50
40. The robust version of EM-algorithm for finite normal mixtures
    
    Vestnik TVGU. Ser. Prikl. Matem. [Herald of Tver State University. Ser. Appl. Math.], 2011, no. 22,  63–72
41. Median modification of EM- and SEM-algorithms for separation ofmixtures of probability distributions and their application to the decomposition of volatility of financial time series
    
    Inform. Primen., 2:4 (2008),  12–47