Abstract
The study is directed towards development of 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 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 favorable 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.
Highlights
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
Identification of possible uncertainties 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]
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 instruments for banking system due to the fact they provide a possibility for detecting “bad” clients and to reduce financial risks caused by the clients
Summary
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 set of the principles includes the following ones: constructing DSS according to the hierarchical strategy of decision making; application of optimization and adaptation techniques for model building, forecasting and control; identification of possible uncertainties (the factors of negative influence 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]. The most important for practical use are the principles of adaptation, optimization and minimization of uncertainty influence that are helpful for enhancing adequacy of the models being constructed and improving the quality of intermediate and final results
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
