All issues

This paper presents a model mixture of probability distributions of signal and noise. Typically, when analyzing the data under conditions of uncertainty it is necessary to use nonparametric tests. However, such an analysis of nonstationary data in the presence of uncertainty on the mean of the distribution and its parameters may be ineffective. The model involves the implementation of a case of a priori non-parametric uncertainty in the processing of the signal at a time when the separation of signal and noise are related to different general population, is feasible.

The number of papers addressing the forecasting of the infectious disease morbidity is rapidly growing due to accumulation of available statistical data. This article surveys the major approaches for the shortterm and the long-term morbidity forecasting. Their limitations and the practical application possibilities are pointed out. The paper presents the conventional time series analysis methods — regression and autoregressive models; machine learning-based approaches — Bayesian networks and artificial neural networks; case-based reasoning; filtration-based techniques. The most known mathematical models of infectious diseases are mentioned: classical equation-based models (deterministic and stochastic), modern simulation models (network and agent-based).

The article deals with the nonlinear model of population dynamics of different ages workers in the regional economy. The model is built on the principles underlying modeling in econophysics. The authors demonstrate the complex dynamics of the model regimes that impose fundamental limits on medium- and long-term forecast of employment in a region. By analogy with the biophysical approach the authors propose a classification of social interactions of the different age-group workers. The model analysis is given for the level of employment among age groups. The verification of the model performs on the statistical data of the Jewish Autonomous Region.

Using modern object-oriented programming language C# a program for simulation of operation of the conveyor for flexible automated assembly of PC was developed. Class diagram of the simulation model of a flexible automated assembly line for PC assembly in mass production mode is presented. Simulation results analysis is presented.

The paper proposes an approach to simulation of a sport game, consisting of a discrete set of separate competitions. According to this approach, such a competition is considered as a random processes, generally — a non-Markov’s one. At first we treat the flow of the game as a Markov’s process, obtaining recursive relationship between the probabilities of achieving certain states of score in a tennis match, as well as secondary indicators of the game, such as expectation and variance of the number of serves to finish the game. Then we use a simulation system, modeling the match, to allow an arbitrary change of the probabilities of the outcomes in the competitions that compose the match. We, for instance, allow the probabilities to depend on the results of previous competitions. Therefore, this paper deals with a modification of the model, previously proposed by the authors for sports games with continuous time.

The proposed approach allows to evaluate not only the probability of the final outcome of the match, but also the probabilities of reaching each of the possible intermediate results, as well as secondary indicators of the game, such as the number of separate competitions it takes to finish the match. The paper includes a detailed description of the construction of a simulation system for a game of a tennis match. Then we consider simulating a set and the whole tennis match by analogy. We show some statements concerning fairness of tennis serving rules, understood as independence of the outcome of a competition on the right to serve first. We perform simulation of a cancelled ATP series match, obtaining its most probable intermediate and final outcomes for three different possible variants of the course of the match.

The main result of this paper is the developed method of simulation of the match, applicable not only to tennis, but also to other types of sports games with discrete time.

Financial mathematics is one of the most natural applications for the statistical analysis of time series. Financial time series reflect simultaneous activity of a large number of different economic agents. Consequently, one expects that methods of statistical physics and the theory of random processes can be applied to them.

In this paper, we provide a statistical analysis of time series of the FOREX currency market. Of particular interest is the comparison of the time series behavior depending on the way time is measured: physical time versus trading time measured in the number of elementary price changes (ticks). The experimentally observed statistics of the time series under consideration (euro–dollar for the first half of 2007 and for 2009 and British pound – dollar for 2007) radically differs depending on the choice of the method of time measurement. When measuring time in ticks, the distribution of price increments can be well described by the normal distribution already on a scale of the order of ten ticks. At the same time, when price increments are measured in real physical time, the distribution of increments continues to differ radically from the normal up to scales of the order of minutes and even hours.

To explain this phenomenon, we investigate the statistical properties of elementary increments in price and time. In particular, we show that the distribution of time between ticks for all three time series has a long (1-2 orders of magnitude) power-law tails with exponential cutoff at large times. We obtained approximate expressions for the distributions of waiting times for all three cases. Other statistical characteristics of the time series (the distribution of elementary price changes, pair correlation functions for price increments and for waiting times) demonstrate fairly simple behavior. Thus, it is the anomalously wide distribution of the waiting times that plays the most important role in the deviation of the distribution of increments from the normal. As a result, we discuss the possibility of applying a continuous time random walk (CTRW) model to describe the FOREX time series.

The temporal variability of exergy of short-wave and long-wave radiation and its relationships with sensible heat, water vapor (H₂O) and carbon dioxide (CO₂) fluxes on a recently clear-cut area in a mixed coniferous and small-leaved forest in the Tver region is discussed. On the basis of the analysis of radiation and exergy efficiency coefficients suggested by Yu.M. Svirezhev it was shown that during the first eight months after clearcutting the forest ecosystem functions as a "heat engine" i.e. the processes of energy dissipation dominated over processes of biomass production. To validate the findings the statistical analysis of temporary variability of meteorological parameters, as well as, daily fluxes of sensible heat, H₂O and CO₂ was provided using the trigonometrical polynomials. The statistical models that are linearly depended on an exergy of short-wave and long-wave radiation were obtained for mean daily values of CO₂ fluxes, gross primary production of regenerated vegetation and sensible heat fluxes. The analysis of these dependences is also confirmed the results obtained from processing the radiation and exergy efficiency coefficients. The splitting the time series into separate time intervals, e.g. “spring–summer” and “summer–autumn”, allowed revealing that the statistically significant relationships between atmospheric fluxes and exergy were amplified in summer months as the clear-cut area was overgrown by grassy and young woody vegetation. The analysis of linear relationships between time-series of latent heat fluxes and exergy showed their statistical insignificance. The linear relationships between latent heat fluxes and temperature were in turn statistically significant. The air temperature was a key factor improving the accuracy of the models, whereas effect of exergy was insignificant. The results indicated that at the time of active vegetation regeneration within the clear-cut area the seasonal variability of surface evaporation is mainly governed by temperature variation.

The method of balanced identification was used to describe the response of Net Ecosystem Exchange of CO₂ (NEE) to change of environmental factors, and to fill the gaps in continuous CO₂ flux measurements in a sphagnum peat bog in the Tver region. The measurements were provided in the peat bog by the eddy covariance method from August to November of 2017. Due to rainy weather conditions and recurrent periods with low atmospheric turbulence the gap proportion in measured CO₂ fluxes at our experimental site during the entire period of measurements exceeded 40%. The model developed for the gap filling in long-term experimental data considers the NEE as a difference between Ecosystem Respiration (RE) and Gross Primary Production (GPP), i.e. key processes of ecosystem functioning, and their dependence on incoming solar radiation (Q), soil temperature (T), water vapor pressure deficit (VPD) and ground water level (WL). Applied for this purpose the balanced identification method is based on the search for the optimal ratio between the model simplicity and the data fitting accuracy — the ratio providing the minimum of the modeling error estimated by the cross validation method. The obtained numerical solutions are characterized by minimum necessary nonlinearity (curvature) that provides sufficient interpolation and extrapolation characteristics of the developed models. It is particularly important to fill the missing values in NEE measurements. Reviewing the temporary variability of NEE and key environmental factors allowed to reveal a statistically significant dependence of GPP on Q, T, and VPD, and RE — on T and WL, respectively. At the same time, the inaccuracy of applied method for simulation of the mean daily NEE, was less than 10%, and the error in NEE estimates by the method was higher than by the REddyProc model considering the influence on NEE of fewer number of environmental parameters. Analyzing the gap-filled time series of NEE allowed to derive the diurnal and inter-daily variability of NEE and to obtain cumulative CO₂ fluxs in the peat bog for selected summer-autumn period. It was shown, that the rate of CO₂ fixation by peat bog vegetation in August was significantly higher than the rate of ecosystem respiration, while since September due to strong decrease of GPP the peat bog was turned into a consistent source of CO₂ for the atmosphere.

This article solves the problem of constructing a neuro-fuzzy model of fuzzy rules formation and using them for objects state evaluation in conditions of uncertainty. Traditional mathematical statistics or simulation modeling methods do not allow building adequate models of objects in the specified conditions. Therefore, at present, the solution of many problems is based on the use of intelligent modeling technologies applying fuzzy logic methods. The traditional approach of fuzzy systems construction is associated with an expert attraction need to formulate fuzzy rules and specify the membership functions used in them. To eliminate this drawback, the automation of fuzzy rules formation, based on the machine learning methods and algorithms, is relevant. One of the approaches to solve this problem is to build a fuzzy neural network and train it on the data characterizing the object under study. This approach implementation required fuzzy rules type choice, taking into account the processed data specificity. In addition, it required logical inference algorithm development on the rules of the selected type. The algorithm steps determine the number and functionality of layers in the fuzzy neural network structure. The fuzzy neural network training algorithm developed. After network training the formation fuzzyproduction rules system is carried out. Based on developed mathematical tool, a software package has been implemented. On its basis, studies to assess the classifying ability of the fuzzy rules being formed have been conducted using the data analysis example from the UCI Machine Learning Repository. The research results showed that the formed fuzzy rules classifying ability is not inferior in accuracy to other classification methods. In addition, the logic inference algorithm on fuzzy rules allows successful classification in the absence of a part of the initial data. In order to test, to solve the problem of assessing oil industry water lines state fuzzy rules were generated. Based on the 303 water lines initial data, the base of 342 fuzzy rules was formed. Their practical approbation has shown high efficiency in solving the problem.

Migration has a significant impact on the shaping of the demographic structure of the territories population, the state of regional and local labour markets. As a rule, rapid change in the working-age population of any territory due to migration processes results in an imbalance in supply and demand on labour markets and a change in the demographic structure of the population. Migration is also to a large extent a reflection of socio-economic processes taking place in the society. Hence, the issues related to the study of migration factors, the direction, intensity and structure of migration flows, and the prediction of their magnitude are becoming topical issues these days.

Mathematical tools are often used to analyze, predict migration processes and assess their consequences, allowing for essentially accurate modelling of migration processes for different territories on the basis of the available statistical data. In recent years, quite a number of scientific papers on modelling internal and external migration flows using mathematical methods have appeared both in Russia and in foreign countries in recent years. Consequently, there has been a need to systematize the currently most commonly used methods and tools applied in migration modelling to form a coherent picture of the main trends and research directions in this field.

The presented review considers the main approaches to migration modelling and the main components of migration modelling methodology, i. e. stages, methods, models and model classification. Their comparative analysis was also conducted and general recommendations on the choice of mathematical tools for modelling were developed. The review contains two sections: migration modelling methods and migration models. The first section describes the main methods used in the model development process — econometric, cellular automata, system-dynamic, probabilistic, balance, optimization and cluster analysis. Based on the analysis of modern domestic and foreign publications on migration, the most common classes of models — regression, agent-based, simulation, optimization, probabilistic, balance, dynamic and combined — were identified and described. The features, advantages and disadvantages of different types of migration process models were considered.