标题: | 运用机器学习方法分析比特币交易行为与货币汇率之交互关系 A machine learning approach to analyze the relationship between Bitcoin transaction behavior and currency exchange rates |
作者: | 刘铭骐 陈安斌 黄思皓 Liu, Ming-Chi Chen, An-Pin Huang, Szu-Hao 资讯管理研究所 |
关键字: | 比特币;自适应增强;类神经网路;汇率;支援向量回归;Bitcoin;Adaboost;Exchange rate forecasting;Affinity Propagation;Neural Network;Support Vector Machine |
公开日期: | 2017 |
摘要: | 身为最大的数位货币,比特币有着许多传统货币没有的特色,包含了极高的波动度,去中心化的管理方式以及区块链分类帐本,其中在区块链这部分,记录着从比特币诞生到今日的每一笔交易,这使得比特币比一般的现实货币拥有了更多的资讯,可以藉由分析这些交易者的纪录来得知一些隐藏的知识。本研究中测试了数种货币特征、六个交易者特征以及三个机器学习方法,期望能找出比特币汇率变动最精准的预测及建模方式。而最后结果显示,现实货币汇率对比特币汇率的影响微乎其微,比特币交易人的行为对比特币汇率的影响则非常的大,而最适合用来预测比特币汇率的模型是Adaboost。 As the most popular digital currency, Bitcoin has its own unique characteristics, such as decentralize management and blockchain ledger technology. In addition, its volatility is much greater than traditional currencies. The blockchain records every transition since the beginning of Bitcoin. From the perspective of big data analytics, Bitcoin contains rich and valuable transition information, which may help the discovery of hidden knowledge about transistors. Our research is expected to precisely predict the exchange rate of Bitcoin through six different features from transistors, three different machine learning algorithms, and various kinds of currency attributes. The experimental results show that the proposed system can effectively model the relationship between the exchange rate change and the transition behavior of Bitcoin. It also displays that traditional currencies seldom influence the change rate of Bitcoin, while Bitcoin users’ behavior is an important factor that makes tremendous difference when forecasting Bitcoin change rate. Finally, compared to artificial neural networks and support vector machines, the learning model based on Adaboost algorithm can achieve the most accurate prediction results. |
URI: | http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070453424 http://hdl.handle.net/11536/141523 |
显示于类别: | Thesis |