Name
Kymratova Alfira Menligulovna
Scholastic degree
•
Academic rank
—
Honorary rank
—
Organization, job position
Karachaevo-Circassian state technological academy
Web site url
Articles count: 16
In the article the forecasting model which is based on the theory of cellular automatic machines and mathematical apparatus of indistinct sets is presented. Its work on the real data of time number productivities of sugar beet in Mostovskoy area of Krasnodar territory is shown
Tools and mathematical methods offered for usage represent essentially new base for forecasting of discrete evolutionary processes. Authors represent complete system of models and methods of temporary ranks’ with memory forecasting
The article discusses the impact of seasonal and event-component time series to assess the predictive performance of the tourist flow in Dombay village in the Karachay-Cherkessia Republic
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
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
This work is devoted to the methods of multicriteria optimization and classical statistics of obtaining pre-estimated information for time series that have long-term memory, which is why their levels do not satisfy the independence property, and therefore the classical prediction methods may be inadequate. The developed methods of obtaining such information are based on classical statistics methods such as mathematical statistics, multicriteria optimization and extreme value theory. The effectiveness of the proposed approach has been demonstrated on the example of specific time series of volumes of mountain rivers
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
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
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 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.