Virtual currency revenue forecasting based on neural network model

This paper aims to explore the virtual currency revenue forecasting method based on the neural network model, through the analysis of the virtual currency market, combined with the powerful ability of the neural network in the forecasting field to design a virtual currency revenue forecasting model. Based on a large number of historical data, the model can accurately predict the return of virtual currency in the future period of time through the training and learning of neural network. In the stage of data collection and processing, the historical transaction data of various virtual currencies are collected and pre-processed, such as cleaning and normalization. Based on these data, the model is trained to continuously optimize its predictive performance. The experimental results show that the model has significant accuracy and stability in predicting virtual currency returns. Compared with the traditional forecasting methods, the neural network model can capture the nonlinear characteristics of market fluctuations better, thus improving the prediction accuracy.