Gold is a very important commodity in today's global world. Therefore, the price of gold and its development is a fundamental question for many researchers. This paper aims to perform a regression analysis of the development of the afternoon price of gold on the New York Stock exchange using artificial neural networks and linear regression. Data from a period longer than ten years are used. This is a total of 2,578 pieces of data. We use linear regression with the linear, exponential, polynomial, logarithmic, numbers of weighted distances, multiple negative-exponential extermination and spline function. Multilayer perceptron neural networks and neural networks of the radial basis function are generated. A total of 1,000 neural structures are generated, 5 of those with the best characteristics are retained Regarding simple linear regression, the curve obtained via the spline function mirrors the development of the gold price best. However, better results are achieved by all 5 preserved neural networks.
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