ВИКОРИСТАННЯ ІНТЕРНЕТ-ТЕХНОЛОГІЙ ДЛЯ ДІАГНОСТИКИ ТА ПРОГНОЗУВАННЯ ПРОЦЕСІВ ПРИЙНЯТТЯ БІЗНЕС-РІШЕНЬ

Abstract

The article analyzes the application of fractal analysis and neural networks in internet technologies for diagnosing the state of a studied object and forecasting time series. These methods will expand the possibilities of using predictions for the future behavior of the researched object in the fields of finance, investment, economics, sociology, and other industries where obtaining prospective knowledge is required. The authors provide an overview of predictive analysis theories and modern econometric and mathematical methods for modeling time series. The justification for choosing fractal analysis as an effective method for diagnosing business decision-making processes is given. A methodology for using internet technology to automate time series forecasting is proposed. A web application has been developed as a software product to solve the research tasks. The process of developing the web application is examined, which would forecast the financial indicators of Ukrainian banks based on available time series. The main stages of development and architecture of the web application for diagnosing and forecasting business processes are described. A generalized structure of software modules is presented. Time series of net profit for Ukrainian banks have been selected as the base model for analysis and forecasting. The developed web application is a complex of programs that use a neural network for time series forecasting. Fractal analysis is used in the stage of forming input data for the neural network. The application also provides the ability to conduct R/S analysis of time series to determine the Hurst exponent. The software product also allows training the neural network using backpropagation. With the machine learning algorithms used in the web application, it is possible to analyze large volumes of data and discover complex dependencies and patterns in time series that may be difficult to notice manually. This leads to more accurate and faster results and enables making more objective forecasts based on the available data. The developed web application can also be used for predicting crises in financial and investment activities.