Bitcoin Transaction Forecasting with Deep Network Representation Learning

TL;DR

This paper introduces DLForecast, a deep learning-based system that models Bitcoin transaction networks using graph embeddings and temporal features to predict transactions with over 60% accuracy, outperforming static models.

Contribution

The paper presents a novel deep neural network approach leveraging temporal graph embeddings and ensemble methods for Bitcoin transaction forecasting.

Findings

Forecasting accuracy exceeds 60%.

Model improves performance by 50% over static graph baselines.

Efficient with fast runtime.

Abstract

Bitcoin and its decentralized computing paradigm for digital currency trading are one of the most disruptive technology in the 21st century. This paper 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\% and improves the prediction performance by 50\% when compared to forecasting model built on the static graph baseline.