论文标题
使用网络和部分微分方程来预测比特币价格
Using Networks and Partial Differential Equations to Predict Bitcoin Price
论文作者
论文摘要
在过去的十年中，区块链技术及其比特币加密货币受到了极大的关注。比特币在每日和长期估值方面经历了重大的价格波动。在本文中，我们在比特币交易网络上提出了一个部分微分方程（PDE）模型，以预测比特币价格。通过对比特币子图或链条的分析，PDE模型随时间捕获了交易模式对比特币价格的影响，并结合了所有连锁店簇的效果。此外，Google趋势指数已纳入PDE模型，以反映比特币市场情绪的影响。该实验表明，2017年连续362天，每日比特币价格预测的平均准确性为0.82。结果表明，PDE模型能够预测比特币价格。该论文是将PDE模型应用于比特币交易网络以预测比特币价格的首次尝试。
Over the past decade, the blockchain technology and its Bitcoin cryptocurrency have received considerable attention. Bitcoin has experienced significant price swings in daily and long-term valuations. In this paper, we propose a partial differential equation (PDE) model on the bitcoin transaction network for predicting bitcoin price. Through analysis of bitcoin subgraphs or chainlets, the PDE model captures the influence of transaction patterns on bitcoin price over time and combines the effect of all chainlet clusters. In addition, Google Trends Index is incorporated to the PDE model to reflect the effect of bitcoin market sentiment. The experiment shows that the average accuracy of daily bitcoin price prediction is 0.82 for 362 consecutive days in 2017. The results demonstrate the PDE model is capable of predicting bitcoin price. The paper is the first attempt to apply a PDE model to the bitcoin transaction network for predicting bitcoin price.
