PROBLEMS OF RETRO-FORECASTS OF ECONOMIC INDICATORS BASED ON NEURAL NETWORKS

Abstract

Introduction. TThe difficult economic situation in Ukraine, the state of emergency, the war and the consequences of the destruction of critical infrastructure have significantly increased the value of economic and mathematical forecasting tools based on neural networks and their tuning capabilities, is improved. The purpose of the article is to analyze and study the potential and formal aspects of the application of neurocomputer methods of economic forecasting and tools to support retro-forecasts of economic indicators. Results. The success of economic decisions (strategic and tactical) in one way or another depends on the quality of analytics and the efficiency of the operational apparatus of decision-making. Even if a rigorous algorithmic approach is difficult or impossible and it is fundamentally impossible to get the right solution, there are effective methods and tools for solving economic problems, an important place among which is forecasting. Just in recent years there has been a breakthrough in the theory and practice of economics and mathematics, many organizations have begun to actively use neural networks in forecasting. Such neural networks can identify patterns by which they generate recommendations for action, they can study and summarize past experiences to improve their own level of performance and calculate forecasts. Neural network methodologies in general and neuro-forecasting in particular belong to the family of machine learning technologies. The specifics of solving the problem of machine learning in forecasting differs from other methods designed for the formation and use of predictions. Possibilities of accumulation and improvement of experience, formation and adaptation of neural network architecture to specifics of forecasting tasks, wide possibilities of application of retro-forecasting methods for improvement of characteristics of adjustment of neural models for forecasts, availability of flexible mechanisms of parameterization and optimization of algorithmic providing forecasting in economic research. Machine learning today is a field of scientific knowledge that is developing rapidly and deals with algorithms capable of learning and developing, which distinguishes this field of research from many others and makes it, at the same time focused on implementation practice, including forecasting. The need to use machine learning methods is due to the fact that for many complex – “intelligent” – tasks (eg, construction and improvement of predictive models) is very difficult (or even impossible) to develop an “explicit” algorithm for solving them, but often you can teach a computer to learn solving these problems with the help of neural networks and retro-forecasting technologies. Conclusions. The formalization of the integrated representation of the neural network for forecasting in the form of activation functions with the definition of conditions for their use in machine learning algorithms, taking into account the specifics of the refined settings of retro-predicted neural models of economic indicators. Demonstrated by the example of retro-forecasting for the simplest neural network (4-2-3-1), when the choice of machine learning algorithm (between PROP and RPROP) is achieved more than twice the effect of improving the quality of the forecast model.