Bitcoin Transaction Forecasting With Deep Network Representation Learning

doi:10.1109/TETC.2020.3010464

Return

Bitcoin Transaction Forecasting With Deep Network Representation Learning

2021-07-01

Wei, WQ *

Qi Zhang

Ling Liu (Ling Liu)

DOI：10.1109/TETC.2020.3010464

Original

Save

View PDF

Abstract

En 中文

Bitcoin and its decentralized computing paradigm for digital currency trading are one of the most disruptive technology in the 21st century. This article presents a novel approach to developing a Bitcoin transaction forecast model, DLForecast, by leveraging deep neural networks for learning Bitcoin transaction network representations. DLForecast makes three original contributions. First, we explore three interesting properties between Bitcoin transaction accounts: topological connectivity pattern of Bitcoin accounts, transaction amount pattern, and transaction dynamics. Second, we construct a time-decaying reachability graph and a time-decaying transaction pattern graph, aiming at capturing different types of spatial-temporal Bitcoin transaction patterns. Third, we employ node embedding on both graphs and develop a Bitcoin transaction forecasting system between user accounts based on historical transactions with built-in time-decaying factor. To maintain an effective transaction forecasting performance, we leverage the multiplicative model update (MMU) ensemble to combine prediction models built on different transaction features extracted from each corresponding Bitcoin transaction graph. Evaluated on real-world Bitcoin transaction data, we show that our spatial-temporal forecasting model is efficient with fast runtime and effective with forecasting accuracy over 60 percent and improves the prediction performance by 50 percent when compared to forecasting model built on the static graph baseline.

Keywords:

Bitcoin

Forecasting

Predictive models

Feature extraction

Peer-to-peer computing

Neural networks

Data models

Network representation learning

large-scale and dynamic graph mining

transaction forecasting as a service

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.