论文标题
基于区块链交易图的基于比特币价格预测的机器学习方法
A Blockchain Transaction Graph based Machine Learning Method for Bitcoin Price Prediction
论文作者
论文摘要
作为最受欢迎的加密货币之一，比特币最近引起了投资者的很多关注。因此，比特币价格预测任务是提供有价值的见解和建议的一个不断上升的学术主题。现有的比特币预测主要基于琐碎的功能工程，该工程手动设计了来自多个领域的功能或因素，包括BTICOIN区块链信息，金融和社交媒体情感。功能工程不仅需要太多的人力努力，而且无法保证直观设计的功能的有效性。在本文中，我们旨在挖掘在比特币交易中编码的丰富模式，并提出K级交易图以揭示不同范围下的模式。我们建议基于事务图的功能自动编码模式。提出了一种新颖的预测方法来接受特征并做出价格预测，从不同历史时期的特定模式中可以利用它。比较实验的结果表明，所提出的方法的表现优于最新的最新方法。
Bitcoin, as one of the most popular cryptocurrency, is recently attracting much attention of investors. Bitcoin price prediction task is consequently a rising academic topic for providing valuable insights and suggestions. Existing bitcoin prediction works mostly base on trivial feature engineering, that manually designs features or factors from multiple areas, including Bticoin Blockchain information, finance and social media sentiments. The feature engineering not only requires much human effort, but the effectiveness of the intuitively designed features can not be guaranteed. In this paper, we aim to mining the abundant patterns encoded in bitcoin transactions, and propose k-order transaction graph to reveal patterns under different scope. We propose the transaction graph based feature to automatically encode the patterns. A novel prediction method is proposed to accept the features and make price prediction, which can take advantage from particular patterns from different history period. The results of comparison experiments demonstrate that the proposed method outperforms the most recent state-of-art methods.
