论文标题
通过交易网络的相关张量光谱投射XRP价格爆发
Projecting XRP price burst by correlation tensor spectra of transaction networks
论文作者
论文摘要
在数字经济时代，加密货物变得至关重要。 XRP是大型市值的加密集结之一。在这里，我们为动态XRP网络开发了一种新型的相关张量光谱方法，该方法可以为XRP价格提供早期指示。 XRP钱包中称重的定向每周交易网络是通过将所有交易汇总一周来构建的。然后，通过将每周网络嵌入连续矢量空间中获得每个节点的向量。根据节点向量的每周快照，我们构建了一个相关张量。相关张量的双重值分解给出了其奇异值。通过与其随机对应物进行比较，显示了奇异值的重要性。奇异值的演变显示出独特的行为。最大的单数值显示与XRP/USD价格显着的负相关。在2018年1月的第一周，我们观察到XRP/USD价格峰值最小的最低奇异值。通过分解信号和噪声组件中的相关张量以及社区结构的演化，可以解释2018年1月最大的奇异值。
Cryptoassets are becoming essential in the digital economy era. XRP is one of the large market cap cryptoassets. Here, we develop a novel method of correlation tensor spectra for the dynamical XRP networks, which can provide an early indication for XRP price. A weighed directed weekly transaction network among XRP wallets is constructed by aggregating all transactions for a week. A vector for each node is then obtained by embedding the weekly network in continuous vector space. From a set of weekly snapshots of node vectors, we construct a correlation tensor. A double singular value decomposition of the correlation tensors gives its singular values. The significance of the singular values is shown by comparing with its randomize counterpart. The evolution of singular values shows a distinctive behavior. The largest singular value shows a significant negative correlation with XRP/USD price. We observe the minimum of the largest singular values at the XRP/USD price peak during the first week of January 2018. The minimum of the largest singular value during January 2018 is explained by decomposing the correlation tensor in the signal and noise components and also by evolution of community structure.
