Precious Metals Price Forecasting Using Neural Networks

Forecasting the stock market is a very challenging task due to the volatile, nonlinear, dynamic and chaotic nature of stock price time series. Stock forecasting is an important area of research in finance because investing in the stock market involves high risk.. With the development of intelligent methods, such as neural networks, the risk can be reduced.Due to the inherent ability of neural networks to detect patterns in nonlinear dynamic chaotic systems, these methods are superior to traditional methods of analyzing and forecasting precious metal prices. Neural networks allow the creation of multivariate models, parallel processing of input data, which allows for the rapid processing of large volumes of data. Their main advantage is that they can identify patterns and anomalies in data, as well as their ability to detect multivariate and nonlinear relationships in this data. This makes neural networks suitable for forecasting market prices.The paper presents a neural network for forecasting prices of precious metals. The constructed model is used for forecasting by analyzing data with preliminary network training based on this data. Using various neural network architectures, we conduct an analysis of specific financial time series.