Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction

International Conference on Blockchain and Trustworthy Systems Pub Date : 2022-04-28 DOI:10.48550/arXiv.2205.00974

Panpan Li, Sheng Gong, Shaocong Xu, Jiajun Zhou, Yu Shanqing, Qi Xuan

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引用次数: 2

Abstract

Blockchain finance has become a part of the world financial system, most typically manifested in the attention to the price of Bitcoin. However, a great deal of work is still limited to using technical indicators to capture Bitcoin price fluctuation, with little consideration of historical relationships and interactions between related cryptocurrencies. In this work, we propose a generic Cross-Cryptocurrency Relationship Mining module, named C2RM, which can effectively capture the synchronous and asynchronous impact factors between Bitcoin and related Altcoins. Specifically, we utilize the Dynamic Time Warping algorithm to extract the lead-lag relationship, yielding Lead-lag Variance Kernel, which will be used for aggregating the information of Altcoins to form relational impact factors. Comprehensive experimental results demonstrate that our C2RM can help existing price prediction methods achieve significant performance improvement, suggesting the effectiveness of Cross-Cryptocurrency interactions on benefitting Bitcoin price prediction.

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用于比特币价格预测的跨加密货币关系挖掘

区块链金融已经成为世界金融体系的一部分，最典型的表现就是对比特币价格的关注。然而，大量的工作仍然局限于使用技术指标来捕捉比特币的价格波动，很少考虑相关加密货币之间的历史关系和相互作用。在这项工作中，我们提出了一个通用的跨加密货币关系挖掘模块，名为C2RM，它可以有效地捕获比特币和相关山寨币之间的同步和异步影响因素。具体而言，我们利用动态时间翘曲算法提取超前-滞后关系，得到超前-滞后方差核，该方差核将用于聚合山寨币信息，形成关系影响因子。综合实验结果表明，我们的C2RM可以帮助现有的价格预测方法实现显着的性能改进，这表明跨加密货币交互对比特币价格预测的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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International Conference on Blockchain and Trustworthy Systems

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