Tensor time series change-point detection in cryptocurrency network data
加密货币网络数据中的张量时间序列变点检测AI 解读 ↓
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加密货币网络数据中的张量时间序列变点检测
- 研究问题
- 当交易活动跨越多个加密货币网络时，如何检测网络之间二阶依赖结构的多重变化，尤其是频繁且幅度较小的变化？
- 方法与思路
- 作者提出 TenSeg，先对张量数据进行分解，再检测分解后数据的交叉协方差或网络结构变点，并用于模拟和以太坊交易数据。
- 材料陈述的贡献
- 摘要称，该方法计算较快、能处理频繁的小幅变化，在所做比较中优于其他变点方法，并发现多个交易网络共同变化的时点。
- 阅读关注点
- 应核对张量分解设定、变点间距与幅度条件、比较方法和调参公平性，以及检测出的网络变化与市场操纵证据之间的关系。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给出性能数值，网络变点本身也不能据此确认为欺诈或市场操纵。
原文摘要
Financial fraud has been growing exponentially in recent years. The rise of cryptocurrencies as an investment asset has simultaneously seen a parallel growth in cryptocurrency scams. To detect possible cryptocurrency fraud, and in particular market manipulation, previous research focused on the detection of changes in the network of trades; however, market manipulators are now trading across multiple cryptocurrency platforms, making their detection more difficult. Hence, it is important to consider the identification of changes across several trading networks or a `network of networks' over time. To this end, in this article, we propose a new change-point detection method in the network structure of tensor-variate data. This new method, labeled TenSeg, first employs a tensor decomposition, and second detects multiple change-points in the second-order (cross-covariance or network) structure of the decomposed data. It allows for change-point detection in the presence of frequent changes of possibly small magnitudes and is computationally fast. We apply our method to several simulated datasets and to a cryptocurrency dataset, which consists of network tensor-variate data from the Ethereum blockchain. We demonstrate that our approach substantially outperforms other state-of-the-art change-point techniques, and the detected change-points in the Ethereum data set coincide with changes across several trading networks or a `network of networks' over time. Finally, all the relevant \textsf{R} code implementing the method in the article are available on https://github.com/Anastasiou-Andreas/TenSeg.