Machine learning of multilayer forecasting models of stock indicators

Abstract

The importance of predicting the price of gold using artificial intelligence systems is consid-ered. Proofs concerning the importance of accurate forecasting of price trends in the gold indus-try, which is a key factor for investors, financial institutions, and economic analysts, are pro-vided. Using AI in this context can help to improve risk management strategies and decision-making in financial markets. Today, people use intelligent monitoring to obtain information about the properties of an object or process by creating and using a model knowledge base while processing the results of observations. When using intelligent monitoring to forecast financial indicators, there is a need to synthesize forecasting models based on limited information about the process history. Each future value of the forecasted indicator is determined by factors that took place in the past. The modeling of exchange bond pricing processes occurs under both structural and informational uncertainty. In order to reduce the uncertainty of the process of forecasting stock indicators, the paper presents the research results using a new method of ma-chine learning as an additional structural element in combination with already existing algo-rithms for synthesizing models of a multilayer agent synthesizer of predictors. The use of a new element does not always improve the characteristics of the system as a whole. The hypothesis about the improvement of the characteristics of model agent synthesizers when using a new ma-chine learning method as a structural element of the layer was tested. For example, the process of forecasting prices of gold on the stock exchange was studied. Simultaneously with the crea-tion of new methods of machine learning, it is proposed to change the structure of the multi-layer synthesizer of models. It was investigated how the new properties of the structural ele-ment of one of the layers change the structure of the agent synthesizer of models as a whole. The research results prove the effectiveness of the process of building a new method of machine learning as a structural element of a multi-layer agent synthesizer of models.