School of Public Administration, China University of Geosciences, Wuhan 430074, China.
School of Public Finance and Taxation, Capital University of Economics and Business, Beijing 100070, China.
Comput Intell Neurosci. 2022 Jun 10;2022:1265837. doi: 10.1155/2022/1265837. eCollection 2022.
As a new type of electronic currency, bitcoin is more and more recognized and sought after by people, but its price fluctuation is more intense, the market has certain risks, and the price is difficult to be accurately predicted. The main purpose of this study is to use a deep learning integration method (SDAE-B) to predict the price of bitcoin. This method combines two technologies: one is an advanced deep neural network model, which is called stacking denoising autoencoders (SDAE). The SDAE method is used to simulate the nonlinear complex relationship between the bitcoin price and its influencing factors. The other is a powerful integration method called bootstrap aggregation (Bagging), which generates multiple datasets for training a set of basic models (SDAES). In the empirical study, this study compares the price sequence of bitcoin and selects the block size, hash rate, mining difficulty, number of transactions, market capitalization, Baidu and Google search volume, gold price, dollar index, and relevant major events as exogenous variables uses SDAE-B method to compare the price of bitcoin for prediction and uses the traditional machine learning method LSSVM and BP to compare the price of bitcoin for prediction. The prediction results are as follows: the MAPE of the SDAE-B prediction price is 0.016, the RMSE is 131.643, and the DA is 0.817. Compared with the other two methods, it has higher accuracy and lower error, and can well track the randomness and nonlinear characteristics of bitcoin price.
作为一种新型的电子货币，比特币越来越被人们所认可和追捧，但它的价格波动较为剧烈，市场存在一定风险，价格较难准确预测。本研究主要目的是采用深度学习集成方法（SDAE-B）对比特币价格进行预测。该方法融合了两种技术：一种是先进的深度神经网络模型，称为堆叠去噪自编码器（SDAE）。SDAE 方法用于模拟比特币价格与其影响因素之间的非线性复杂关系。另一种是强大的集成方法，称为自助聚合（Bagging），它可以为一组基本模型（SDAES）生成多个数据集进行训练。在实证研究中，本研究比较了比特币的价格序列，并选择了区块大小、哈希率、挖矿难度、交易数量、市值、百度和谷歌搜索量、黄金价格、美元指数以及相关重大事件作为外生变量，使用 SDAE-B 方法对比特币价格进行预测，并使用传统的机器学习方法 LSSVM 和 BP 对比特币价格进行预测。预测结果如下：SDAE-B 预测价格的 MAPE 为 0.016，RMSE 为 131.643，DA 为 0.817。与其他两种方法相比，它具有更高的准确性和更低的误差，能够很好地跟踪比特币价格的随机性和非线性特征。
