Tensor time series change-point detection in cryptocurrency network data

Abstract Financial fraud has been growing exponentially in recent years. The rise of cryptocurrencies as an investment asset has seen a parallel growth in cryptocurrency scams. To detect possible cryptocurrency fraud, 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 a ‘network of networks’ over time. To this end, we propose a new change-point detection method, labeled TenSeg, in the network structure of tensor-variate data. 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. TenSeg substantially outperforms state-of-the-art change-point techniques, and the detected change-points for Ethereum coincide with changes across several trading networks. All the relevant R code implementing the method in the article is available on https://github.com/Anastasiou–Andreas/TenSeg.