#### Name

Kymratova Alfira Menligulovna

#### Scholastic degree

•

#### Academic rank

—

#### Honorary rank

—

#### Organization, job position

Karachaevo-Circassian state technological academy

#### Web site url

## Articles count: 16

This article discusses the ways of reducing the financial, economic and social risks on the basis of an accurate prediction. We study the importance of natural time series of winter wheat yield, minimum winter, winter-spring daily temperatures. The feature of the time series of this class is disobeying a normal distribution, there is no visible trend

In rapidly changing conditions of the modern world,
analysts and decision makers are in need to use new
formal means of analysis and evaluation of alternatives
problems. This work is dedicated to the development
of such tools. The article presents a detailed analysis
and technical and economic characteristics of the
subject area - the financial market and its specific
components - the value of a time series of gold, silver,
palladium, platinum, and two kinds of exchange rates:
EUR / RUB, USD / RUB. The authors have proposed
a 5-criteria economic-mathematical model of the main
components of the ranking of the financial market. The
authors argue the impossibility of using a single
integrated set of criteria for the replacement of the
criteria or the use of criteria convolution procedures as
the standard procedure of solving the problem of
multi-criteria optimization. It demonstrates that such
criteria as criteria for "risk" must be considered as an
estimate of the degree of deviation from the expected
value of the possible values of this criterion. The
practical significance of the results is determined by
the fact that the main points, conclusions,
recommendations, models and methods can be used in
order to improve the management and planning of
development strategies of banking systems, trading
platforms, as well as by developers of information and
analytical systems to support management decisionmaking

#### ARTIFICIAL INTELLIGENCE METHODS FOR DECISION MAKING AND PREDICTING THE BEHAVIOR OF DYNAMICAL SYSTEMS

This article proposes a modification and training the Cellular Automaton predictive model. The author presents a modified system of models and methods for time series prediction with memory based on the theory of fuzzy sets and linear cellular automata

It is offered to expand the classification of risks by
introducing a global risk of economic system,
which separates stages burdened with the local
risks having arbitrarily direction. Serial or parallel
origin of these risks is modeled dyadic chain
vectors or four-dimensional conglomerates of
quaternions in Clifford spaces. Multivariate risk is
to transform analytically, calculate quantitatively,
construct geometric vector operations in the
ensemble with the economic variables on which
part of the cost of the risk and that is lost or after
symptoms appear. Therefore, the cost of an asset
depends on a comprehensive cost of the "basis",
burdened risk ("common value"), and the
magnitude of the risk of leaving part - "risky value"
- from zero. Now, the risk emerges as a new
economic and mathematical category. Through the
study of risks and through research of their new
multi-dimensional performance value it is possible
to insight into understanding the mechanisms of
action of the economic laws worldwide and in
Russia

The present study was carried out in the view of the
fact that there is no more or less complete theory of
time series prediction memory to date. This determines
the urgency and necessity of the development of new
mathematical methods and algorithms to detect
possible potential predictability of the series with the
memory and the construction of adequate predictive
models. Classical methods of forecasting economic
time series are based on the mathematical apparatus of
econometrics. It is carried out basing on the
assumption that the observations that make up the
projected time series are independent, whereby to
perform the necessary subordination of the normal
law. The latter, however, is the exception rather than
the rule for economic time series that have so-called
long-term memory. Toolkit implementations of
nonlinear dynamics were the new computer
technology that made it possible to study complex
phenomena and processes “on the display screen”. The
proposed approach differs from the classical methods
of forecasting by the implementation of a new
accounting trends (evolution of centers and the size of
a bounding box), and is a new tool (phase portraits) to
identify the cyclical components of the considered
time series

The article studies the degree of "riskiness" of natural time series, which are inherent properties of the seasonal trend. The authors have made an analysis the result of which is the effect relationship between weather conditions and the dynamics of the behavior of the monthly volumes of mountain rivers

In the context of the objective existence of risk and
economic, human and other losses related with it, there
is a need in a specific mechanism, which would allow
the best way to predict the damage caused by the
emergency. These risk management tools in
emergency situations are monitoring and forecasting.
In this research work, time series are used as a signal;
they contain information about the number of fires in
the Karachayevo-Cherkessia in the period of 1983-
2014. In solving the problem, the authors applied
wavelet tools for data cleaning from noise, anomalies
that have provided quality model building reliable
forecast - possible number of fires in one quarter
ahead. This example shows that for the construction of
this forecast there is no need for a rigorous
mathematical model specification, which is especially
valuable in the analysis of poorly formalized
processes. We have noted that most of the tasks in
emergencies fall into this category of processes

Development of monitoring of the behavior of
financial market, simulation, analysis, visualization,
prediction in modern conditions is connected with a
consistent increase in their level of formalization. The
basis for this process is the requirements of
significantly changed (in the direction of increasing)
stochastics, turbulence, volatility, financial and
economic processes. Particular relevance in the
analysis of behavior of economic time series elements
of the financial market is now becoming more
systematic development of diverse, interdependent and
mutually complementary economic and mathematical
models. The models are linked, they are operating on
the same source material, and their selection has
improved the representativeness of the algorithms of
modern economic processes of the financial market,
which is important for transformational (transitional)
market economies. In the article it is shown that the
proposed usage of instrumentation and mathematical
methods represent essentially new base for forecasting
of discrete evolutionary processes

#### PREDICTION OF A FINANCIAL MARKET EVOLUTION ON THE BASE OF SOFTWARE TOOLS FOR LINEAR CELLULAR AUTOMAT

The work used methods of system analysis, monographic, structural and logical, economic-statistical, mathematical continuous and discrete, settlement and constructive methods as well as software tools of linear cellular automata. Usage of each method was based on their functionality, thus ensuring the accuracy of the findings and scientific positions. In this article we attempt to predict the dynamic behavior of the financial market elements, to use on the basis of a linear cellular automaton computer tools and methods of nonlinear science for adequate numerical reflection measure various risks, primarily financial and economic risks, as well as to show the power of computer graphics, computer mathematics system linear cellular automata, to emphasize an important philosophical role of visualization. The authors of the work programmed linear cellular automaton based on Python 2.7 software platform in the form of application. The program validates the predictive model on the adequacy of the selected coloring, is forecast error and builds polygons predictive model and input data on the same graph. The proposed research area is relevant to the processes in the financial and economic system, bringing in useful innovative elements in the generalized forecast that do not exist in continuous classical methodology

The increase in volume of processed data and the rapid
development of environmental monitoring, modeling,
forecasting, analysis, visualization, prediction in
modern conditions is connected with the consistent
increase in their level of formalization. The bases for
all this are requirements of significantly changed
stochastics natural and economic processes. A new
method of nonlinear dynamics, namely the method of
sequential R/S-analysis is proposed. In the article, the
authors paid attention to the method of fractal analysis
of time series. The founder of fractal analysis is a
British hydrologist H.E Hurst. He showed that natural
phenomena such as river flows, rainfall, temperature,
solar activity is followed by «biased random walk»,
i.e. trend with noise. The noise level and trend
resistance are estimated in change in the normalized
amplitude levels of the time series for the expiration
time, or, in other words, how they entered a quantity
called the Hurst exponent exceeds the value of 0.5.
Rather essential information is a cyclical component to
forecast. Thus, there is a need for further study of
natural and economic processes based on the new
mathematical models. These methods bring to forecast
new useful methodological elements that are not in
continuous methodology, concepts such as «noise
color» persistence and anti-persistent series, Hurst,
«long-term memory», R/S-trajectory and the trajectory
of the Hurst exponent, etc.