Zhiheng Jiang
ABSTRACT
After Covid-19 swept the globe and bitcoin prices suddenly soared, machine learnings were used to predict the trend of bitcoin prices, but these studies were lack of performance analysis in different time-scale span. In this paper, three neural network models are designed and used to forecast the price of bitcoin after the outbreak of COVID-19. The models A uses the high/low price, open/close price of four-days of bitcoin as input variables and the close price of the fifth day as target variable, the models B uses same variable as the model A and uses optimal weights, and the model C uses same structure as the model B, but adds the trading volume to the input variables. The results show that the model C may lower the difference between actual and calculated outputs, thus boosting the prediction accuracy. Also, it is found that the models that can work well when predicting bitcoin prices in a short time span can be obviously less precise when it comes to predicting bitcoin prices in a longer time span.
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