INFORMATION SYSTEM FOR FORECASTING NONLINEAR NON-STATIONARY PROCESSES IN FINANCE

Background. Financial processes are often characterised by nonlinearity and non-stationarity, which makes them difficult to accurately model and forecast. Traditional methods cannot effectively take into account the complex interrelationships and variability of such processes, which generates increased uncertainty and risks. This leads to the need to develop new information systems and methods to improve the accuracy and sustainability of forecasts. Objective. To provide a brief overview of the characteristics of nonlinear non-stationary processes, to develop a methodology for their modelling, as well as to build mathematical models based on actual statistical data and to obtain practically useful results of modelling and forecasting selected processes. Methods. The methodology for modelling and forecasting nonlinear non-stationary processes is applied, models are built using data mining, such as regression models and a neural network, and the main metrics for assessing the adequacy of the model and quality of the forecast are used. Results. The developed information system for modelling and forecasting nonlinear non-stationary processes is approbated on real statistical data. Based on data mining methods, models of the share price dynamics of a well-known company were built. The study's results demonstrate that using an integrated approach, which includes regression models and neural networks, significantly improves the quality of forecasting variance changing in time and the nonlinear non-stationary process. Conclusions. The task of high-quality forecasting of processes due to rapid, sometimes hard-to-predict changes in the external environment, i.e. external shocks, which is typical for nonlinear non-stationary financial processes, is still relevant today. The literature provides a sufficient variety of methods for modelling these processes. However, in this research, the methods that have demonstrated their advantages in modelling financial transactions in the stock market were chosen, and therefore it makes sense to expand and improve the perspectives of this approach.