Адаптивне прогнозування та оцінювання фінансових ризиків
The study is directed towards development of an adaptive decision support system for modeling and forecasting nonlinear nonstationary processes in economy, finances and other areas of human activities. The structure and parameter adaptation procedures for the regression and probabilistic models are...
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| author | Danilov, Valery Ya. Gozhyj, O. P. Kalinina, I. O. Belas, Andrii O. Bidyuk, Petro I. Jirov, O. L. |
| author_facet | Danilov, Valery Ya. Gozhyj, O. P. Kalinina, I. O. Belas, Andrii O. Bidyuk, Petro I. Jirov, O. L. |
| author_institution_txt_mv | [
{
"author": "Valery Ya. Danilov",
"institution": "Educational and Scientific Complex \"Institute for Applied System Analysis\" of the National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
},
{
"author": "O. P. Gozhyj",
"institution": "Petro Mohyla Black Sea National University, Mykolayiv"
},
{
"author": "I. O. Kalinina",
"institution": "Petro Mohyla Black Sea National University, Mykolayiv"
},
{
"author": "Andrii O. Belas",
"institution": "Educational and Scientific Complex \"Institute for Applied System Analysis\" of the National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
},
{
"author": "Petro I. Bidyuk",
"institution": "Educational and Scientific Complex \"Institute for Applied System Analysis\" of the National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
},
{
"author": "O. L. Jirov",
"institution": "Educational and Scientific Complex \"Institute for Applied System Analysis\" of the National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
}
] |
| author_sort | Danilov, Valery Ya. |
| baseUrl_str | http://journal.iasa.kpi.ua/oai |
| collection | OJS |
| datestamp_date | 2020-08-11T08:50:57Z |
| description | The study is directed towards development of an adaptive decision support system for modeling and forecasting nonlinear nonstationary processes in economy, finances and other areas of human activities. The structure and parameter adaptation procedures for the regression and probabilistic models are proposed as well as the respective information system architecture and functional layout are developed. The system development is based on the system analysis principles such as adaptive model structure estimation, optimization of model parameter estimation procedures, identification and taking into consideration of possible uncertainties met in the process of data processing and mathematical model development. The uncertainties are inherent to data collecting, model constructing and forecasting procedures and play a role of negative influence factors to the information system computational procedures. Reduction of their influence is favourable for enhancing the quality of intermediate and final results of computations. The illustrative examples of practical application of the system developed proving the system functionality are provided. |
| doi_str_mv | 10.20535/SRIT.2308-8893.2020.1.04 |
| first_indexed | 2025-07-17T10:26:42Z |
| format | Article |
| fulltext |
V. Danilov, O. Gozhyj, I. Kalinina, A. Belas, P. Bidyuk, O. Jirov, 2020
34 ISSN 1681–6048 System Research & Information Technologies, 2020, № 1
UDC 004.942+519.816
DOI: 10.20535/SRIT.2308-8893.2020.1.04
ADAPTIVE FORECASTING
AND FINANCIAL RISK ESTIMATION
V. DANILOV, O. GOZHYJ, I. KALININA, A. BELAS, P. BIDYUK, O. JIROV
Absract. The study is directed towards development of an adaptive decision support
system for modeling and forecasting nonlinear nonstationary processes in economy,
finances and other areas of human activities. The structure and parameter adaptation
procedures for the regression and probabilistic models are proposed as well as the
respective information system architecture and functional layout are developed. The
system development is based on the system analysis principles such as adaptive
model structure estimation, optimization of model parameter estimation procedures,
identification and taking into consideration of possible uncertainties met in the process
of data processing and mathematical model development. The uncertainties are in-
herent to data collecting, model constructing and forecasting procedures and play
a role of negative influence factors to the information system computational proce-
dures. Reduction of their influence is favourable for enhancing the quality of inter-
mediate and final results of computations. The illustrative examples of practical ap-
plication of the system developed proving the system functionality are provided.
Keywords: economic and financial processes, adaptive modeling, forecasting
nonlinear nonstationary processes, uncertainties, system analysis, decision support
system.
INTRODUCTION
Modeling and forecasting financial, economic, ecological, climatology and many
processes in other spheres of human activity is important problem that is to be
solved by many companies and institutions in business, at the state and industrial
enterprises, scientific and educational laboratories etc. The most distinctive com-
mon features of such processes today are non-stationarity and nonlinearity that
require a lot of special attention for estimating respective model structure and its
parameters. To improve the forecasts based upon mathematical models it is neces-
sary to develop new appropriate model structures that would adequately describe
the processes under study and provide a possibility for computing high quality
forecasts. One of the most promising modern approaches to modeling and fore-
casting is based upon so called systemic approach that supposes application of
system analysis principles in the frames of specialized decision support system
(DSS) [1–3]. Usually the set of the principles includes the following ones: – con-
structing DSS according to the hierarchical strategy of decision making; – appli-
cation of optimization and adaptation techniques for model building, forecasting
and control; – identification of possible uncertainties (the factors of negative in-
fluence to the computational procedures used in DSS that are of various kind and
origin) and application of algorithmic means helping to reduce their influence on
the quality of intermediate and final results of data processing and decision making
[4]. Some other systemic principles could be hired for constructing DSS, though
perhaps not so important as those mentioned above. The most important for prac-
Adaptive forecasting and financial risk estimation
Системні дослідження та інформаційні технології, 2020, № 1 35
tical use are the principles of adaptation, optimization and minimization of uncer-
tainty influence that are helpful for enhancing adequacy of the models being con-
structed and improving the quality of intermediate and final results.
There are many positive examples of adaptation and optimization techniques
application in modeling, forecasting and control [5–7]. This is especially urgent
task for analyzing non-stationary processes met practically in all the areas
mentioned above. There are two basic directions of adaptation while solving the
modeling problems: adaptation of model structure and parameters. According to
our definition the notion of model structure includes the following elements:
model dimension that is determined by the number of its equations; model order
that is determined by the highest order of a model equation; output reaction delay
time (or lag) for independent variables (regressors); system or process
nonlinearity and its type (nonlinearity with respect to variables or to parameters);
type of external stochastic disturbance (distribution and its parameters); system
(process) initial conditions and possible restrictions on variables and/or model
parameters [8]. Thus, we have many possibilities for model structure corrections
and its adaptation to varying system operation modes and conditions of
application.
The books [6–8] consider various possibilities for mathematical models
adaptation and their further applications to short-term forecasting dynamics of
specific processes under consideration. The set of possible model structures
proposed is very wide, starting from linear regression equations and up to
sophisticated probabilistic models in the form of Bayesian networks, various
nonlinear structures and combined models. There also can be found some
adaptation procedures illustrating possible changes of a model structure and re-
computing of their parameters. It is stressed that application of adaptation
schemes helps to increase model adequacy in changing conditions of random ex-
ternal influences, nonlinearity and non-stationarity of the process under study.
The study [9] describes procedures for constructing adaptive regression
models on the basis of large datasets. The authors proposed development of
decision rules in application to machine learning. They stress that model trees and
regression rules are most expressive approaches for data mining procedures of
model development. The adaptive model rules proposed in the study create a one-
pass algorithm that can adapt available set of rules to the possible changes in the
processes under consideration. The sets of rules generated can be ordered or
unordered, and it was shown experimentally that unordered rules exhibited higher
performance in the terms of statistical quality parameters of the models generated.
The results presented in [10–11] consider the problem of adaptive models
constructing for nonstationary heteroscedastic processes widely known today in
analysis of financial time series. The authors proposed a procedure for automated
constructing and model selection in finance. The flexible procedure is general-to-
specific modeling of the mean, variance and probabilistic distribution. The initial
specification of a model starts from autoregressive terms and regressors
(explanatory variables). The variance specification is based upon log-ARCH and
log-GARCH terms, the term of asymmetry, Bernoulli jumps and other possible
explanatory variables. The algorithm developed returns specifications of
parsimonious mean and variance as well as standardized error distribution in
cases when normality is rejected. The extensive Monte Carlo simulations were
performed and three empirical applications studied that support usefulness of the
method proposed for practical analysis of financial data.
V. Danilov, O. Gozhyj, I. Kalinina, A. Belas, P. Bidyuk, O. Jirov
ISSN 1681–6048 System Research & Information Technologies, 2020, № 1 36
The use of adaptive exponential smoothing for lumpy demand forecasting is
considered in [12]. It showed substantial advantages over some conventional ap-
proaches used in practice due to appropriate selecting the model smoothing factor.
Kalman filter is used to perform preliminary measurement data processing, and
then forecasting models are constructed using adaptive smoothing factor based
upon optimal filter weighting function. As a result the model performance with
this weighting function managed to generate smaller forecasting errors than their
counterparts used in demand prediction.
Adaptive forecasting of dynamic processes in conditions when recent and
ongoing structural changes are present is considered in [13], and the nature of the
changes is unknown. The authors used the method of down-weighting older data
based on the tuning parameter found as a result of minimizing mean square error
of time series forecasts. A detailed theoretical analysis of the forecasting method
is presented as well as positive results of multiple computational experiments
based upon macroeconomic data from US economy.
The problem of short-term forecasting in conditions of availability of struc-
tural breaks is considered in [14]. The optimal one step ahead forecasts are gener-
ated using known exponential smoothing techniques. Analytical expressions are
derived for optimal weights in models with one and multiple regressors. The au-
thors showed that the weight remains the same within a given operating regime of
a system under study. The comparative study of the method proposed was per-
formed using Monte Carlo simulations and the data from industrial economies.
It was shown that robust optimal weights provide high quality short-term fore-
casts when information on structural breaks is uncertain.
A short review of adaptive approaches to modeling and forecasting processes
in various areas of human activities presented above indicates that appropriate
adaptation of the models constructed usually helps to construct adequate models
and to enhance forecast quality. The study proposed is directed towards develop-
ment of adaptive forecasting system providing a possibility for forecasting non-
linear nonstationary processes (NNPs) met in economy, finances, ecology etc.
PROBLEM STATEMENT
The purpose of the study is in solving the following problems: to develop struc-
ture and parameter adaptation procedures for the regression and probabilistic
models; to develop the system architecture for modeling and forecasting nonlinear
nonstationary processes in economy, finances, ecology and other areas based on
the system analysis principles; to consider possibilities for elimination of some
uncertainties inherent in data collecting, model constructing and forecasting pro-
cedures; to develop the methodology for modeling and forecasting linear and non-
linear processes in the frames of the same system; providing illustrative examples
of practical application of the system developed proving the system functionality.
SOME COMMON FEATURES OF THE PROCESSES IN ECONOMY,
FINANCES AND ECOLOGY
A wide diversity of various processes exists in economy, finances, ecology, de-
mography and other areas of human activity. However, there are some general
common features of the process like linearity/nonlinearity, and stationar-
Adaptive forecasting and financial risk estimation
Системні дослідження та інформаційні технології, 2020, № 1 37
ity/nonstationarity that allow to divide them into practically understandable
classes and select appropriate modeling and forecasting techniques. Fig. 1 shows
simplified classification of the processes from which we could make a conclusion
about wide variety of mathematical model structures that could be applied for
formal description of the processes dynamics and solving the problem of fore-
casting their evolution in time.
Linear processes can be stationary without trend and nonstationary when
they contain linear (first order) trend, )1(I , where )1(I means integrated of the
first order. If variance (covariance) of stochastic linear process is time dependent
then it is classified as heteroscedastic and requires nonlinear models for describ-
ing the process variance and possibly the process itself.
There also exists a wide variety of nonlinear processes though we selected
only some of them that are more frequent in economy and finances. Generally the
processes can be split into nonlinear regarding parameters and nonlinear re-
garding variables. The first type is more sophisticated with respect to modeling
and parameter estimation and usually requires more efforts and time for their
model development; it is not considered here. As an example of such a model
could be mentioned widely used in practice logistic regression.
Some nonlinear processes can exhibit linear behavior in their stable (nomi-
nal) mode of operation. This feature allows for linear description of the process in
the vicinity of operating point. Generally NNPs are very often met in the areas of
study mentioned above. The set of the processes includes integrated processes
(IP) that contain a trend of order two or higher as well as cointegrated processes
with the trends of the same order, and the processes with time changing variance,
i. e. heteroscedastic processes. Most of financial processes illustrating price evo-
lution of stock instruments belong to this class [15, 16]. In engineering applica-
tions such processes are studied in diagnostic systems where appropriate decision
is made regarding current system state.
Dynamic processes in economy,
finances, ecology and other areas
Linear processes Nonlinear processes
Stationary Nonstationary Piecewise
stationary
Nonstationary
Integrated
Cointegrated
Heteroscedastic
Linearly
integrated
Fig. 1. A simplified classification of dynamic processes in economy and finances
V. Danilov, O. Gozhyj, I. Kalinina, A. Belas, P. Bidyuk, O. Jirov
ISSN 1681–6048 System Research & Information Technologies, 2020, № 1 38
METHODOLOGY OF MODELING NONLINEAR NONSTATIONARY PROCESSES
The methodology proposed for modeling NNPs illustrates Fig. 2. At the first step
of the methodology the data collected is subjected to preliminary processing that
may include the following basic operations: imputation of missing observations,
Preliminary
data processing
Data
Extra data
Trend type
identification
Analysis of
ACF and
PACF
Correlation
matrix
analysis
Mutual
covariance
analysis
Identification
of nonlinearity
type
identification
Lag
estimation
Disturbance
type (distrib.)
estimation
Methods of modeling and forecasting: regression analysis, Kalman filter,
neural nets, GMDH, fuzzy GMDH, probabilistic models, fuzzy logic,
support vector regression, nearest neighbour
Model structure and parameter estimation
Model quality
is acceptable?
Yes
No `A set of model
adequacy criteria
Forecasting function construction
Forecast estimate computing,
combining of the forecasts
Quality
of forecast is
acceptable?
A set of forecast
quality criteria
No
Heteroskedasticity
analysis
1 – data normalizing
2 – smoothing
3 – filtering
4 – data imputation
5 – extreme value processing
Analysis
of seasonal
effects
Comparison
of the results
computed
1) determination coefficient, R2
2) Durbin-Watson, DW
3) Akaike Inform. Criterion (AIC)
4) t-statistics
5) SSE
6) Fisher F – statistic
Principal
component analysis
1) MSE
2) MAPE
3) Theil coefficient
4) Mean abs. error
Yes
Fig. 2. Functional layout of the forecasting system proposed
Adaptive forecasting and financial risk estimation
Системні дослідження та інформаційні технології, 2020, № 1 39
normalization in a given range, digital or optimal filtering dependently on prob-
lem statement, principal component analysis, appropriate processing of outliers
etc. Here it is also appropriate to perform identification and elimination (reduc-
tion) of data uncertainties that may touch the following: non-measurable value
estimation; computing the general statistical parameters (variance, covariance,
mean, median etc.); performing data structuring according to the problem state-
ment; analysis of distribution types and their parameters; estimation of prior
probabilities where necessary [17, 18].
Estimation of a model structure using statistical and probabilistic (mutual)
information analysis that provides a possibility for estimation of the following
elements of a model structure: dimension of a model – number of equations creating
the model; model order (highest order of difference or differential equation of the
model); nonlinearity and its type; estimate of input delay time, and type of prob-
abilistic distribution for the model variables. It is always appropriate to perform
structure estimation for several candidate-models so that to have a possibility
for selecting the best one of the candidates estimated.
Formally, to detect nonlinearity in statistical data available statistical tests
and techniques should be applied. Fig. 3 shows some known techniques for test-
ing the data for nonlinearity.
Along with application known technics we proposed a new simplified em-
pirical criterion for detecting nonlinearity in data that is shown below in the
Fig. 3: here R is maximum deviation of the process under study from its linear
approximation; is sample standard deviation of the process. It does not require
Nonlinearity detection
Known techniques
Analysis
of spectral
function
Variance
analysis based
method
Correlation
procedures for
nonlinearity
analysis
Generalized
variable
approach
Fisher test
on
nonlinearity
Preliminary
filtering and
extra data
processing
is required
Requires
solving of
complex inte-
gral
equation
Complex
computing
of functions
represented by
Volterra series
Does not
require complex
computing, can
be applied to
short samples
Preliminary
data
processing
is required
Proposed
simplified
empirical
criterion
Empirical
nonlinearity
criterion:
/maxRD
Simple
computations,
can be applied
to short samples
Fig. 3. Some techniques for testing data for nonlinearity
V. Danilov, O. Gozhyj, I. Kalinina, A. Belas, P. Bidyuk, O. Jirov
ISSN 1681–6048 System Research & Information Technologies, 2020, № 1 40
sophisticated computations though provides for additional information about
availability of nonlinearity.
The sequence of operations allowing for constructing nonlinear model illus-
trates Fig. 4; actually this is a part of general model constructing procedure given
in Fig. 2.
Possible nonlinearities (with respect to model variables) could be taken in
the following way: first linear part is estimated using known linear structures like
autoregressive equations with moving average with linear trend, paired or
multiple regression etc. Then nonlinear part of the model is added in the form of
nonlinear trend, quadratic, bilinear or higher order terms, nonlinear terms describ-
ing cyclic changes of the main variable etc. The modeling practice shows that ac-
ceptable model adequacy can be often reached using combination of linear and
nonlinear regression, linear regression and Bayesian networks, linear regression
and special nonlinear functions like nonparametric kernels. Using this approach a
set of candidate models could be constructed with subsequent selecting the best
one using appropriate set of statistical adequacy criteria as shown in Fig. 2. Un-
fortunately formal possibilities for determining in a unique way the type of non-
linearity not always exist, especially when the data samples are short.
The next step is model parameter estimation by making use of alternative
techniques; in linear case these are the following ones: ordinary least squares
Preliminary data processing
Testing data for nonlinearity
Estimation of linear model structure
Selection of terms
for describing nonlinear component
Is nonlinear
model adequate?
Forecast estimation
Yes
No
Data
Fig. 4. Procedure for formal describing nonlinear process
Adaptive forecasting and financial risk estimation
Системні дослідження та інформаційні технології, 2020, № 1 41
(OLS) and its clones, maximum likelihood (ML) and many others. In a case of
nonlinear model estimation the following methods are useful: ML, Markov Chain
Monte Carlo (MCMC) procedures [19], nonlinear least squares (NLS) and other
suitable approaches able to provide unbiased parameter estimates under specific
probabilistic distributions of model variables and model structures. Correct appli-
cation of alternative parameter estimation techniques provides a possibility for
further comparison of the candidate models and selection of the best one. It is also
possible to trace the reasons for existing parametric uncertainties in the following
form: parameter estimates computed with statistical data cannot be consistent,
they may contain bias, and can be inefficient. All these effects finally result in
poor adequacy of the model constructed.
At the next stage is computed a set of statistical parameters characterizing
model quality (adequacy) and selecting the most suitable model out of the set of
candidate models. There is no need to leave only one model for computing fore-
casts (or solving control problem). Again, it can be a set of the “best” models con-
structed on different ideologies. The final choice is always made after models ap-
plication for solving the problem according to the initial problem statement.
After computing the process (under study) forecasts using candidate models
another set of forecast quality criteria is applied to select the best result, say mean
absolute percentage error (MAPE), Theil coefficient, mean absolute error (MAE),
minimum and maximum errors of forecasting etc. The models constructed should
also be tested on similar process, i.e. model calibration process performed.
At this point we can conclude that availability of the data uncertainties men-
tioned, and the necessity for hierarchical construction of the data processing sys-
tem with the features of adaptation and optimization (structural and parametric)
require application of the modern systemic approach that provides a possibility
for successful and high quality solving the problems encountered during statistical
data processing, mathematical model construction, forecast estimation and
generating the decision alternatives. In this study we propose some practical pos-
sibilities for constructing data processing procedures based on modern principles
of systemic approach.
Dealing with model structure uncertainties. When using DSS, model
structure should practically always be estimated using data. It means that ele-
ments of the model structure accept almost always only approximate values.
When a model is constructed for forecasting we build several candidates and se-
lect the best one of them using a set of model quality (adequacy) statistics.
Generally we could define the following techniques to fight structural uncertain-
ties: gradual refinement of model order (for AR(p) or ARMA(p, q) structures)
applying adaptive approach to modeling and automatic search for the “best” struc-
ture using complex statistical quality criteria; adaptive estimation of input delay
time (lag) and the type of data distribution with its parameters; formal description
of detected process nonlinearities using alternative analytical forms with subse-
quent estimation of model adequacy and forecast quality. A simple example of the
complex model and forecast criterion may look as follows:
i
MAPEDWRJ
ˆ
2 minln21 ;
or in more complicated form:
V. Danilov, O. Gozhyj, I. Kalinina, A. Belas, P. Bidyuk, O. Jirov
ISSN 1681–6048 System Research & Information Technologies, 2020, № 1 42
i
UMAPEDWkeRJ
N
k
ˆ
1
22 minln2)(ln1 ,
where 2R is determination coefficient; 2
11
2 ])(ˆ)([)( kykyke
N
k
N
k
is a sum of
squared model errors; DW is Durbin-Watson statistic; MAPE is mean absolute
percentage error for one step-ahead forecasts; U is Theil coefficient that charac-
terizes forecasting capability of a model; , are appropriately selected weight-
ing coefficients; î is parameter vector for thi candidate model. A combined
criterion of this type is used for automatic selection of the best candidate model
constructed. The criteria presented also allow operation of DSS in adaptive mode.
Obviously, other forms of the combined criteria are possible dependently on spe-
cific purpose of model building. What is important while constructing the crite-
rion: not to overweigh separate members in right hand side that would suppress
other components.
Coping with uncertainties of a level (amplitude) type. The availability of
random and/or non-measurable variables results in the necessity of hiring fuzzy
sets for describing processes in such situations. The variable with random ampli-
tude can be described with some probability distribution if the measurements are
available or when they come for analysis in acceptable time span. However, some
variables cannot be measured in principle, say amount of shadow capital that
“disappears” every month in offshore zones, or amount of shadow salaries paid at
some company, or a technology parameter that cannot be measures on-line due to
absence of appropriate gauge or in-situ physical difficulties. In such situations it is
possible to assign to the variable a set of characteristic values in linguistic form,
say as follows: capital amount = { very low, low, medium, high, very high }.
There exists a complete set of necessary mathematical operations to be applied to
such fuzzy variables. Finally fuzzy value can be transformed into exact non-fuzzy
form using known transformation techniques.
Probabilistic uncertainties and their description. The use of random vari-
ables leads to the necessity of estimating actual probability distributions and their
application in inference computing procedures. Usually observed value is known
only approximately though we know the limits for the actual values. Appropriate
probability distributions are very useful for describing the processes under study
in such situations. When dealing with discrete outcomes, we assign probabilities
to specific outcomes using a mass function. It shows how much “weight” (or
mass) to assign to each observation or measurement. An answer to the question
about the value of a particular outcome will be its mass. The Kolmogorov’s axi-
oms of probability are helpful for deeper understanding of what is going on. If
two or more variables are analyzed simultaneously it is necessary to construct and
use joint distributions. Joint distributions allow estimation of conditional prob-
abilities using renormalization procedures when necessary.
Very helpful for performing probabilistic computations is a notion of condi-
tional independence: )|()|()|,( zyPzxPzyxP , where x and y are inde-
pendent events. Such identities are very handy though one should be careful when
using them, i.e. the events should be actually independent. The remarkable intui-
Adaptive forecasting and financial risk estimation
Системні дослідження та інформаційні технології, 2020, № 1 43
tive meaning of discrete Bayes’ law, )(/)()/()/( BPAPABPBAP , is that it
allows to ask the reverse questions: “Given that event A happened, what is the
probability that a particular event B evoked it?” The marginal probability, )(BP ,
can be computed using appropriate conditionals. The probability that event B
will occur in general, )(BP , could be obtained from the following condition:
)()/()()/()( APABPAPABPBP .
The probabilistic types of uncertainties regarding whether or not some event
will happen can be taken into consideration with probabilistic models. To solve
the problem of describing and taking into account such uncertainties a variety of
Bayesian models could be hired that are considered as Bayesian Programming
formalism. The set of the models includes Bayesian networks (BN), dynamic
Bayesian networks (DBN), Bayesian filters, particle filters, hidden Markov
models, Kalman filters, Bayesian maps etc. The structure of Bayesian program
includes the following elements: problem description and statement formulation
with a basic question of the form: )/( KnownSearchedP or ),/( KnDXP i , where
iX defines one variable only, i.e. what should be estimated using specific infer-
ence engine; the use of prior knowledge Kn and experimental data D to perform
model structure and parameters identification; selection and application of perti-
nent inference technique to answer the question stated before; testing quality of
the final result. Such approach also works well in adaptation mode aiming to ad-
justing structure and parameters of a model being developed to new experimental
data or a new system operation mode, for example, for estimation of prior distri-
butions or BN structure.
SOME SYSTEM ANALYSIS PRINCIPLES USED IN DSS IMPLEMENTATION
In our study we propose to use the following system analysis principles for im-
plementing specialized DSS for modeling and forecasting: the systemic function
coordination principle; the principle of procedural completeness; the functional
orthogonality principle; the principle of dependence of mutual information
between the functions being implemented; the principle of functional rationality;
the principle of multipurpose generalization; the principle of multifactor adapta-
tion, and the principle of rational supplement [20–22].
The principle of systemic functions coordination supposes that all the tech-
niques, approaches, and algorithms (functions) implemented in the system should
be structurally and functionally coordinated, and should be mutually dependent.
This way it is possible to create and practically implement a unique systemic
methodology for statistical data analysis in the frames of modern DSS, and to im-
prove substantially quality of intermediate and final results. The next systemic
principle of procedural completeness guaranties that the system developed will
provide the possibility for timely and in place execution of all necessary comput-
ing functions directed towards data collection (editing, normalizing, filtering and
renewing), formalization of a problem statement, model constructing, computing
forecasts, and for performing estimation quality of the model and the forecast es-
timates based upon it.
Development and implementation of all computational procedures in the
DSS using mutually independent functions corresponds to the principle of func-
V. Danilov, O. Gozhyj, I. Kalinina, A. Belas, P. Bidyuk, O. Jirov
ISSN 1681–6048 System Research & Information Technologies, 2020, № 1 44
tional orthogonality. Such approach to the DSS constructing is directed towards
substantial enhancement of computational stability of the system and simplifica-
tion of its further possible modifications and functional enhancement. According
to the principle of mutual informational dependence the results of computing,
generated by each procedure, should correspond to the formats and requirements
of other procedures. This feature is easily implemented with respective project
development solutions for the system created.
Application of the systemic principle of goal directed correspondence to
computational procedures and functions provides a good possibility for reaching
of a unique goal set in advance: high (acceptable) quality of the final result in the
form of forecast estimates for the process under study as well as alternative deci-
sions based upon the forecasts.
According to the systemic principle of multipurpose generalization all func-
tional modules for the system developed should possess necessary degree of gen-
eralization that provides a possibility for reaching high quality solution results for
a set of possible problems that belong to the same class (it can be high quality
forecasting and decision alternative generation regarding future evolution of lin-
ear or nonlinear non-stationary processes). Among these problems could be the
following: accumulating necessary data and their preliminary processing; estima-
tion of structure and parameters for a set of candidate mathematical models; con-
structing forecasting functions on the models developed and computing of appro-
priate forecasts; selecting the best results of computing using appropriate sets of
quality criteria.
The systemic principle for multifactor adaptation is directed towards the
possibility of solving the problems of computational procedures adaptation to the
problems of modeling various processes of different complexity depending on the
completeness of available information and user requirements. The adaptation is
performed within the process of model structure and parameters estimation, i.e.
the whole identification procedure of a process under study is compiled from a set
of adaptive procedures directed towards reaching the main goal of a study: con-
structing adequate model and computing high quality forecasts.
Hiring the rational supplement principle provides a possibility for expanding
the sphere of application of the DSS constructed by adding new processes types,
computational procedures and criteria sets. These new procedures could be di-
rected towards implementation of additional preliminary data processing proce-
dures, model structure and parameter estimation as well as selection of the best
result for its further use aiming generating of appropriate decision. Implementa-
tion in the frames of the constructed DSS of the systemic principles mentioned
above favors its functional flexibility, computational reliability, quality enhance-
ment for the intermediate and final results, prolongation of system life span, and
simplification of possible drawbacks elimination and modification procedures.
Finally, the forecasting models and methods used in the system are the fol-
lowing: regression analysis, the group method for data handling (GMDH), fuzzy
GMDH, fuzzy logic, appropriate versions of the optimal Kalman filter (KF), neu-
ral nets, support vector regression, nearest neighbor and probabilistic type tech-
niques like Bayesian networks and regression. The set of modeling techniques
used covers linear and many types of nonlinear non-stationary processes. The
nearest neighbor technique is hired for generating long term forecasts in a case of
Adaptive forecasting and financial risk estimation
Системні дослідження та інформаційні технології, 2020, № 1 45
availability long data samples with periodical patterns. All the techniques are im-
plemented in adaptive versions what makes the system more flexible for newly
coming data and capable to fight some types of possible uncertainties mentioned
above. During the process of model structure estimation an appropriate principal
component analysis technique is applied when necessary.
BAYESIAN NETWORK ADAPTATION
Bayesian networks (BN) create one of the powerful modern probabilistic instru-
ments for solving the problems of mathematical modeling, forecasting, classifica-
tion, control and decision support [23–26]. To estimate BN model structure the
algorithms are used on the basis of statistical data that characterize evolution of
the network variables. It is possible to develop and use the algorithms that allow
for adaptation of the network structure to the new data coming in real time. This
is a choice used in the DSS with adaptation features.
The adaptation procedure could be explained using the following notation:
},...,{ 1 nXXZ is a set of BN model nodes that is determined by the number
of variables hired to construct appropriate directed graph; ,|),({ iji XXXE
}ZX j is a set of BN arcs; iX is a BN node that corresponds to the
observations of one variable; Zn is a total number of BN nodes; ir is a
number of values that could be accepted by the node iX ; ikv is the k-th value of
variable iX ; i is the set of parent nodes for the variable iX ; is the set of
possible initializations i for the node iX ; iiq is the number of possible
initializations i ; ij is j-th initialization for the set of parent nodes i for iX ;
SB is structure of BN; PB is probabilistic specification of BN, i.e. the part
of BN description that represents its probabilistic characteristics,
),|( Pijikiijk BvXp under condition that the sum of the probabilities
1
k
ijk ; ),...,( 1 iijrijf is the probability density for the node iX and
initialization ij ; 0D is database; 0S is preliminary estimate of BN structure
computed on the basis of available data 0D ; 1D is database of observations that
were not used for estimating preliminary structure 0S ; 1S is BN structure found
after 0S adaptation to the new data 1D . The problem is to construct algorithm for
adaptation of initial Bayesian network EZG , having the structure, 0S , to the
new observations 1D .
This way a new (or modified) model structure will be formed: 11 DS . The
statistical data used could exhibit arbitrary probability distribution, and the proc-
esses described by the data could be of nonlinear non-stationary nature i.e. their
mathematical expectation const][ iXE and variance, const]}[{ 2 ii XEXE .
Adaptation of the BN to new data is implemented in the following way:
implementation of the procedure for refining the model structure: here the
model arcs can be eliminated or added;
V. Danilov, O. Gozhyj, I. Kalinina, A. Belas, P. Bidyuk, O. Jirov
ISSN 1681–6048 System Research & Information Technologies, 2020, № 1 46
correcting the probabilistic part of the model (conditional probability
tables or CPTs).
At the initial stage of learning BN the probabilistic part of the model is rep-
resented in the form of CPTs that are computed on the basis of the frequency
analysis of available statistical data. Consider the procedure of correcting this
probabilistic part of the model. For this purpose it is more convenient to save (and
use) the values of ijkN instead of the CPTs themselves, where ijkN is a number
of values corresponding to the, ijk . This way it is possible to perform renewing
the data faster regarding conditional distributions and the values themselves could
be computed using the Dirichlet expression:
iij
ijk
ijiiki rN
N
vXp
1
)|( .
When correcting the BN structure the order of the nodes analysis will be de-
termined by the value that each node provides for the following conditional prob-
ability [27]:
n
i
Q
t
M
u
iit
R
s
Q
t
m
u
its
i it
i i its
urN
uN
SDDp
1
1 1
1 1 1
001
1
),|( .
An informational importance of the model arcs is performed as follows. To
determine the necessity of deleting a node the following value is computed:
)( 0delete SK for the current configuration of the parent nodes set. Also the value of
)( 1delete
mSK is computed for the directed graph configurations that represent the
result of deleting one of M ( Mm 1 ) input arcs for the current node. Under
condition )()( 0delete1delete SKSK m the m-th arc continues to belong to the model
structure because its elimination results in decreasing of the local quality func-
tional (i.e. for the current node). Otherwise the arc is registered in the list of arcs
that should be tested further on for elimination. The further testing is based upon
computing the value of the local functional for initially set configuration (struc-
ture) and for the configurations that result from eliminating of one of the arcs that
still are left in the list.
As far as BN model constructing strategy is based upon the general functional
),|(
),|()|(
maxarg),,|(
001
010
0011 DSDP
DSDPDSP
SDDSP
S
,
the arcs elimination and adding procedure is of optimization type and is per-
formed in the following way. The arc elimination should result in decreasing the
value of the first member in the nominator, )|( 0DSP , because it reaches maxi-
mum with 0SS when initial structure 0S is formed. Generally, to get a positive
effect of adaptation it is necessary to compensate the loss due to arc elimination
by the effect of adding new arc. That is why the search for the arc to be added to
the graph is performed as mentioned above. Estimation of effect due to adding the
arc is also based on the local quality functional, its value should increase [27, 28].
Adaptive forecasting and financial risk estimation
Системні дослідження та інформаційні технології, 2020, № 1 47
EXAMPLES OF THE DSS APPLICATION
Example 1. Numerous examples of model constructing and forecasting have been
solved with the DSS developed. In this example bank client’s solvency is ana-
lyzed, i.e. application scoring is estimated. The database used consisted of 4700
records that were divided into learning sample (4300 records), and test sample
(400 records). The default probabilities were computed and compared to actual
data, and also errors of the first and second type were computed using various
levels of cut-off value. It was established that maximum model accuracy reached
for Bayesian network was 0,787 with the cut-off value 0,3. The Bayesian network
is “inclined to over insurance”, i.e. it rejects more often the clients who could re-
turn the credit. The model accuracy and the errors of type I and type II depend on
the cut-off level selected. The cut-off value determines the lowest probability lim-
it for client’s solvency, i.e. below this limit a client is considered as such that will
not return the credit. Or the cut-off value determines the lowest probability limit
for client’s default, i.e. below this limit a client is considered as such that will re-
turn the credit. As far as the cut-off value of 0,1 or 0,2 is considered as not impor-
tant, in practice it is reasonable to set the cut-off value at the level of about
0,25–0,30. Statistical characteristics characterizing quality of the models con-
structed are given in Table 1.
T a b l e 1 . Adequacy of the models constructed
Model type Gini index AUC Common accuracy Model quality
Bayesian network 0,719 0,864 0,787 (0,806) Very high
Logistic regression 0,685 0,858 0,813 (0,828) Very high
Decision tree 0,597 0,798 0,775 Acceptable
Linear regression 0,396 0,657 0,631 (0,639) Unacceptable
Thus, the best models for estimation of probability for credit return are
logistic regression and Bayesian network. The best common accuracy showed
logistic regression, 0,813, though Bayesian network exhibited higher Gini index,
0,719 (the values in parenthesis show improvement due to application o adaptive
mode of modeling). The decision tree hired is characterized by Gini index of
about 0,597, and CA = 0,775. It should be stressed that acceptable values of Gini
index for developing countries like Ukraine are located usually in the range
between 0,4–0,6. The Bayesian network constructed and nonlinear regression
showed high values of Gini index that are acceptable for the Ukrainian economy
in transition.
Example 2. In this case the following four types of scoring were studied:
application scoring that is based on the data given by clients during the
process of analyzing the possibility for providing them with a loan;
behavioral scoring or scoring analysis within the period of loan usage;
this study was directed to monitoring of a loan keeper account state, in this case
we estimated the probability of timely return of the loan by clients, optimal loan
limit for the loans etc.;
strategic scoring that is directed towards determining the strategy regard-
ing non-reliable loan keepers violating the rules established;
fraud scoring the purpose of which is to determine the probability of po-
tential fraud on behalf of clients.
V. Danilov, O. Gozhyj, I. Kalinina, A. Belas, P. Bidyuk, O. Jirov
ISSN 1681–6048 System Research & Information Technologies, 2020, № 1 48
The database used in this case consisted of 96000 records with 30 tokens for
each client. Some results of computational experiments are presented in Table 2.
T a b l e 2 . Results of computational experiments for application and behavior
scoring
Application scoring Behavior scoring
Model used Mean
AUC
Common
accuracy
Learning
time
Mean
AUC
Common
accuracy
Learning
time
Logistic
regression
0,917 0,873 3,47 0,905 0,854 (0,876) 2,66
Bayesian
network
0,922 0,862 2,98 0,913 0,851 (0,864) 2,86
Gradient
boosting
0,974 0,925 148,64 0,971 0,911 (0,929) 150,78
The table contains common accuracy values for the computational experi-
ments without adaptation and with adaptation in parenthesis. The adaptation mode
has always generated better results than the mode without adaptation feature. For
the purpose of simulating adaptation mode the data were divided into parts of
equal size (3000 records in each part) and then after model constructing and usage
the new data portion was fed into model constructing algorithm.
To analyze strategic scoring the subset of data was used that characterizes
annual income of active clients and their total expenditures according to their
credit cards within a year. The purpose of the study is to divide clients into clus-
ters and to apply a unique management strategy to each cluster using K-means
technique. The basic parameter for using K-means clustering technique is a num-
ber of clusters K. The parameter is estimated using the concept of minimizing
sum of squares criterion within a cluster (WCSS). It was established that six clus-
ters provide for an acceptable clustering of the clients:
K1: an average income and low expenses;
K2: low income and low expenses;
K3: high income and high expenses;
K4: low income and high expenses;
K5: an average income and high expenses;
K6: very high income and high expenses.
The fraud analysis was performed with the highly unbalanced data: 187 op-
erations out of the total number of operations 86754 were classified as the fraud.
The positive class of the data (fraud) included 0,215% of all the operations per-
formed. The Bayesian network constructed on the data showed AUC = 0,863.
After the data was corrected with expanding the smaller class of data (oversam-
pling approach) the result of classification was improved to the following: AUC =
896,0 . Finally a combined approach was applied to solving the problem that
supposes application of oversampling, elimination of “noise” from the observa-
tions, and gradual improvement of balance between the classes to about 40 : 60
and 50 : 50. The result of classification was improved to the AUC = 0,928, and in
adaptation mode to the value of about AUC = 0,935.
Example 3. As an example of methodology application a time series was
studied, the values of which were gold prices within the period between the years
Adaptive forecasting and financial risk estimation
Системні дослідження та інформаційні технології, 2020, № 1 49
2005–2006 (sample contains 504 values). The statistical characteristics showing
constructed models and forecasts quality are given in Table 3. Here the case is
considered when adaptive Kalman filter was not used for preliminary data
processing smoothing.
T a b l e 3 . Models and forecasts quality without adaptive Kalman filter
application
Model quality Forecast quality
Model type
2R )(2 ke DW MSE MAE MAPE Theil
AR(1) 0,99 25644,67 2,15 49,82 41.356 8,37 0,046
AR(1,4) 0,99 25588,10 2,18 49,14 40,355 8,12 0,046
AR(1) + 1st
order trend 0,99 25391,39 2,13 34,39 25,109 4,55 0,032
АР(1,4) +1st
order trend 0,99 25332,93 2,18 34,51 25.623 4,67 0,032
AR(1) + 4th
order trend 0,99 25173,74 2,12 25,92 17,686 3,19 0,024
Thus, the best model turned out to be AR(1) + trend of 4th order. It provides
a possibility for one step ahead forecasting with mean absolute percentage error
of about 3,19%, and Theil coefficient is 024,0U . The Theil coefficient shows
that this model is generally good for short-term forecasting. Statistical characteris-
tics of the models and respective forecasts computed with adaptive Kalman filter
application for data smoothing are given in Table 4. Here optimal filter played
positive role what is supported by respective statistical quality parameters.
T a b l e 4 . Models and forecasts quality with application of adaptive Kalman
filter
Model quality Forecast quality
Model type
2R )(2 ke DW MSE MAE MAPE Theil
AR(1) 0,99 24376,32 2,11 45,21 39,73 7,58 0,037
AR(1,4) 0,99 24141,17 2,09 47,29 38,75 7,06 0,035
AR(1) + 1st
order trend
0,99 23964,73 2,08 31.15 22,11 3,27 0,029
AR(1) + 4th
order trend
0,99 22396,83 2,04 21,35 13,52 2,71 0,019
Again the best model turned out to be AR(1) + trend of 4th order. It provides
a possibility for one step ahead forecasting with mean absolute percentage error
of about 2,71%, and Theil coefficient is: 019,0U . Thus, in this case the results
achieved are better than in previous modeling and short-term forecasting without
filter application.
Example 4. Statistical analysis of the time series selected with application of
Goldfeld-Quandt test proved that gold prices data create heteroscedastic process
with time varying conditional variance. As far as the variance is one of the key
parameters that is used in the rules for performing trading operations it is neces-
sary to construct appropriate forecasting models. Table 5 contains statistical charac-
V. Danilov, O. Gozhyj, I. Kalinina, A. Belas, P. Bidyuk, O. Jirov
ISSN 1681–6048 System Research & Information Technologies, 2020, № 1 50
teristics of the models constructed as well as quality of short-term variance fore-
casting. To solve the problem we used generalized autoregressive conditionally
heteroscedastic (GARCH) models together with description of the processes trend
which is rather sophisticated (high order process). The models of this type
(GARCH) demonstrated low quality of short-term forecasts, and quite acceptable
(EGARCH) one-step ahead forecasting properties. The values of MAPE (adapt.)
given in the 6th column for the mode of operation with adaptation show im-
provement of short term forecasting for conditional variance when modeling sys-
tem operated in the mode with model adaptation.
T a b l e 5 . Results of modeling and forecasting conditional variance
Model quality Forecast quality
Model type
2R )(2 ke DW MSE
MAPE
(adapt.)
MAPE Theil
GARCH(1,7) 0,99 153639 0,113 972,5 515,3 517,6 0,113
GARCH (1,10) 0,99 102139 0,174 458,7 208,2 211,3 0,081
GARCH (1,15) 0,99 80419 0,337 418,3 118,7 121,6 0,058
EGARCH (1,7) 0,99 45184 0,429 67,8 7.85 8,74 0,023
Thus, the best model constructed was exponential GARCH(1,7). The
achieved value of MAPE = 8,74% (and 7,85% in adaptation mode) comprises
very good result for forecasting conditional variance. Further improvements of the
forecasts were achieved with application of the adaptation scheme proposed. An
average improvement of the forecasts was in the range between 0,8–1,5%, what
justifies advantages of the approach proposed. Combination of the forecasts
generated with different forecasting techniques helped to further decrease mean
absolute percentage forecasting error for about 0,5–0,8% in this particular case. It
should be stressed that analysis of heteroscedastic processes is very popular today
due to multiple engineering, economic and financial applications of the models
and forecasts based upon them.
DISCUSSION
The results of computational experiments achieved lead to the conclusion that
today the family of scoring models used including logistic regression, Bayesian
networks and gradient boosting belong to the family of the best current instru-
ments for banking system due to the fact they provide a possibility for detecting
“bad” clients and to reduce financial risks caused by the clients. It also should be
stressed that DSS developed creates very useful instrument for a decision maker
that helps to perform quality processing of client’s statistical data using various
techniques, generate alternatives and to select the best one relying upon a set of
appropriate statistical criteria. An important role in the computational experiments
performed played the possibility of model adaptation to available and new data.
The adaptation mode has always generated better results than the mode without
this adaptation feature. The extra model variables can be created by combining
available statistical data, and nonlinearities can be introduced into a model by in-
serting appropriate polynomial members. The system proposed performs tracking
of the whole computational process using separate sets of statistical quality crite-
ria at each stage (each level of the system hierarchy) of decision making: quality
Adaptive forecasting and financial risk estimation
Системні дослідження та інформаційні технології, 2020, № 1 51
of data, adequacy of models constructed and quality of the forecasts (or risk esti-
mates).
Thus, the systemic approach to modeling and forecasting proposed is
definitely helpful for constructing the DSS possessing the features of directed
search for the best forecasting model in respective spaces of model structures and
parameters, and consequently to enhance its adequacy. The computational
experiments with actual data showed high usefulness of the systemic approach to
modeling and forecasting. It is necessary to perform its further refinement in the
future studies and applications. And it is also important to improve formal
descriptions for the uncertainties mentioned and to use them for reducing the
degree of uncertainty in model building procedures and forecast estimation. It was
found that influence of statistical and probabilistic uncertainties can be reduced
substantially by making use of respective data filtering techniques, imputation of
missing values, orthogonal transforms, and the models of probabilistic type; first
of all those are Bayesian programming models and techniques.
CONCLUSIONS
The systemic methodology was proposed for constructing decision support sys-
tem for adaptive mathematical modeling and forecasting modern economic and
financial processes as well as for credit risk estimation that is based on the
following system analysis principles: hierarchical system structure, taking into
consideration probabilistic and statistical uncertainties, availability of model adap-
tation procedures, generating multiple decision alternatives, and tracking of com-
putational processes at all the stages of data processing with appropriate sets of
statistical quality criteria (known or newly introduced).
The system developed has a modular architecture that provides a possibility
for easy extension of its functional possibilities with new parameter estimation
techniques, forecasting methods, financial risk estimation, and generation of
decision alternatives. High quality of the final result is achieved thanks to
appropriate tracking of the computational processes at all data processing stages:
preliminary data processing, model structure and parameter estimation,
computing of short- and middle-term forecasts, and estimation of risk
variables/parameters. The system is based on the ideologically different methods
of dynamic processes modeling and risk forecasting (regression analysis and
probabilistic approach) what creates appropriate basis for hiring various
approaches to achieve the best results. The illustrative examples of the system
application show that it can be used successfully for solving practical problems of
forecasting dynamic processes evolution and risk estimation. The results of
computational experiments lead to the conclusion that today scoring models,
nonlinear regression and Bayesian networks are the best instruments for banking
system due to the fact that they provide a possibility for detecting “bad” clients
and to reduce financial risks caused by the clients. It also should be stressed that
the DSS constructed turned out to be very useful instrument for a decision maker
that helps to perform quality processing of statistical data using ideologically
different techniques, appropriate sets of statistical quality criteria, generate
alternatives and select the best one. The DSS can be used for supporting decision
making process in various areas of human activities including development of
strategy for banking system regarding risk management and industrial enterprises,
investment companies etc.
V. Danilov, O. Gozhyj, I. Kalinina, A. Belas, P. Bidyuk, O. Jirov
ISSN 1681–6048 System Research & Information Technologies, 2020, № 1 52
Further extension of the system functions is planned with new forecasting
and decision making techniques based on probabilistic methodology, fuzzy sets
and other artificial intelligence methods. An appropriate attention should also be
paid to constructing user friendly adaptive interface based on the human factors
principles.
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Received 04.12.2019
____________________________________
From the Editorial Board: the article corresponds completely to submitted manu-
script.
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| id | journaliasakpiua-article-209133 |
| institution | System research and information technologies |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2025-07-17T10:26:42Z |
| publishDate | 2020 |
| publisher | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
| record_format | ojs |
| resource_txt_mv | journaliasakpiua/ba/ed5a96d7cf71f5a2f1f8249c6901d3ba.pdf |
| spelling | journaliasakpiua-article-2091332020-08-11T08:50:57Z Adaptive forecasting and financial risk estimation Адаптивное прогнозирование и оценивание финансового риска Адаптивне прогнозування та оцінювання фінансових ризиків Danilov, Valery Ya. Gozhyj, O. P. Kalinina, I. O. Belas, Andrii O. Bidyuk, Petro I. Jirov, O. L. економічні та фінансові процеси адаптивне моделювання прогнозування нелінійних нестаціонарних процесів невизначеності системний аналіз система підтримки прийняття рішень экономические и финансовые процессы адаптивное моделирование прогнозирование нелинейных нестационарных процессов неопределенности системный анализ система поддержки принятия решений economic and financial processes adaptive modeling forecasting nonlinear nonstationary processes uncertainties system analysis decision support system The study is directed towards development of an adaptive decision support system for modeling and forecasting nonlinear nonstationary processes in economy, finances and other areas of human activities. The structure and parameter adaptation procedures for the regression and probabilistic models are proposed as well as the respective information system architecture and functional layout are developed. The system development is based on the system analysis principles such as adaptive model structure estimation, optimization of model parameter estimation procedures, identification and taking into consideration of possible uncertainties met in the process of data processing and mathematical model development. The uncertainties are inherent to data collecting, model constructing and forecasting procedures and play a role of negative influence factors to the information system computational procedures. Reduction of their influence is favourable for enhancing the quality of intermediate and final results of computations. The illustrative examples of practical application of the system developed proving the system functionality are provided. Исследование направлено на создание адаптивной системы поддержки принятия решений для моделирования и прогнозирования нелинейных нестационарных процессов в экономике, финансах и других отраслях деятельности человека. Предложены процедуры для адаптивного оценивания структуры и параметров регрессионных и вероятностных моделей, а также архитектура и функциональная схема соответствующей информационной системы. Разработка системы основывается на принципах системного анализа, таких как адаптивное оценивание структуры моделей, оптимизация процедур оценивания параметров моделей, идентификация и учет возможных неопределенностей, которые учитываются при сборе данных, построении моделей, в процедурах прогнозирования и играют роль негативных факторов влияния на вычислительные процедуры в информационной системе. Уменьшение их влияния способствует повышению качества промежуточных и окончательных результатов вычислений. Рассмотрены иллюстративные примеры практического использования разработанной системы, которые подтверждают ее функциональность. Дослідження спрямовано на створення адаптивної системи підтримання прийняття рішень для моделювання і прогнозування нелінійних нестаціонарних процесів в економіці, фінансах та інших галузях людської діяльності. Запропоновано процедури для адаптивного оцінювання структури і параметрів регресійних і ймовірнісних моделей, а також архітектуру і функціональну схему відповідної інформаційної системи. Розроблення системи ґрунтується на принципах системного аналізу, таких як адаптивне оцінювання структури моделей, оптимізація процедур оцінювання параметрів моделей, ідентифікація та врахування можливих невизначеностей, які враховуються під час оброблення даних і побудови математичних моделей, а також для збирання даних, побудови моделей, у процедурах прогнозування і відіграють роль факторів негативного впливу на обчислювальні процедури в інформаційній системі. Зменшення їх впливу сприяє підвищенню якості проміжних та остаточних результатів обчислень. Розглянуто ілюстративні приклади практичного застосування розробленої системи, що підтверджують її функціональність. The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2020-06-23 Article Article application/pdf https://journal.iasa.kpi.ua/article/view/209133 10.20535/SRIT.2308-8893.2020.1.04 System research and information technologies; No. 1 (2020); 34-53 Системные исследования и информационные технологии; № 1 (2020); 34-53 Системні дослідження та інформаційні технології; № 1 (2020); 34-53 2308-8893 1681-6048 en https://journal.iasa.kpi.ua/article/view/209133/209256 Copyright (c) 2021 System research and information technologies |
| spellingShingle | економічні та фінансові процеси адаптивне моделювання прогнозування нелінійних нестаціонарних процесів невизначеності системний аналіз система підтримки прийняття рішень Danilov, Valery Ya. Gozhyj, O. P. Kalinina, I. O. Belas, Andrii O. Bidyuk, Petro I. Jirov, O. L. Адаптивне прогнозування та оцінювання фінансових ризиків |
| title | Адаптивне прогнозування та оцінювання фінансових ризиків |
| title_alt | Adaptive forecasting and financial risk estimation Адаптивное прогнозирование и оценивание финансового риска |
| title_full | Адаптивне прогнозування та оцінювання фінансових ризиків |
| title_fullStr | Адаптивне прогнозування та оцінювання фінансових ризиків |
| title_full_unstemmed | Адаптивне прогнозування та оцінювання фінансових ризиків |
| title_short | Адаптивне прогнозування та оцінювання фінансових ризиків |
| title_sort | адаптивне прогнозування та оцінювання фінансових ризиків |
| topic | економічні та фінансові процеси адаптивне моделювання прогнозування нелінійних нестаціонарних процесів невизначеності системний аналіз система підтримки прийняття рішень |
| topic_facet | економічні та фінансові процеси адаптивне моделювання прогнозування нелінійних нестаціонарних процесів невизначеності системний аналіз система підтримки прийняття рішень экономические и финансовые процессы адаптивное моделирование прогнозирование нелинейных нестационарных процессов неопределенности системный анализ система поддержки принятия решений economic and financial processes adaptive modeling forecasting nonlinear nonstationary processes uncertainties system analysis decision support system |
| url | https://journal.iasa.kpi.ua/article/view/209133 |
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