Understanding Bitcoin Price Prediction Trends under Various Hyperparameter Configurations

【Author】 Kim, Jun-Ho; Sung, Hanul

【Source】COMPUTERS

【影响因子】0.000

【Abstract】Since bitcoin has gained recognition as a valuable asset, researchers have begun to use machine learning to predict bitcoin price. However, because of the impractical cost of hyperparameter optimization, it is greatly challenging to make accurate predictions. In this paper, we analyze the prediction performance trends under various hyperparameter configurations to help them identify the optimal hyperparameter combination with little effort. We employ two datasets which have different time periods with the same bitcoin price to analyze the prediction performance based on the similarity between the data used for learning and future data. With them, we measure the loss rates between predicted values and real price by adjusting the values of three representative hyperparameters. Through the analysis, we show that distinct hyperparameter configurations are needed for a high prediction accuracy according to the similarity between the data used for learning and the future data. Based on the result, we propose a direction for the hyperparameter optimization of the bitcoin price prediction showing a high accuracy.

【Keywords】bitcoin; cryptocurrency; LSTM; deep learning; data prediction; optimization

【发表时间】2022 NOV

【收录时间】2022-12-23

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-价格预测