Romanenkov Y. Scientific bases and information technology for adaptive forecast of technical and economic indicators of organizational and technical systems in the data uncertainty conditions

The object - performance forecasting of organizational and technical systems, the goal - ensuring the forecasting quality in organizational and technical systems by establishing the information technology for multilevel adaptive estimation of their performance under data uncertainty; methods - system analysis principles, mathematical statistics, singular value analysis methods, robust stability and interval analysis theories. An urgent applied scientific problem of ensuring the quality of performance forecasting of organizational and technical systems by establishing the information technology for multilevel adaptive estimation of their performance under data uncertainty was solved. The information technology for multilevel adaptive performance estimation of organizational and technical systems, the method of parametric identification of the Brown model by the results of the retrospective analysis, the method of dynamic aggregation of forecasts were developed for the first time. The method of efficiency estimation of organizational and technical systems using the matrix forecast processing unit was improved. The methodology of forecasting the dynamics of complex organizational and technical objects and systems towards multi-level adaptive estimation of their performance under data uncertainty, the model of service planning in organizational and technical systems, the Brown one-parameter forecasting model by the proposed parameter setting method, the Theil-Wage model for forecasting seasonal processes in organizational and technical systems under limited data retrieval by synthesizing the combined forecasting model on its basis using the SSA model frequency parameters were further developed. The research results were introduced at six domestic enterprises and organizations, as well as three foreign companies. The scope - forecasting support of administrative decisions under uncertainty.