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.

中文翻译：

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使用深度网络表示学习进行比特币交易预测

比特币及其用于数字货币交易的去中心化计算范式是 21 世纪最具颠覆性的技术之一。本文介绍了一种通过利用深度神经网络学习比特币交易网络表示来开发比特币交易预测模型 DLForecast 的新方法。DLForecast 做出了三项原创贡献。首先，我们探索比特币交易账户之间的三个有趣的特性：比特币账户的拓扑连接模式、交易金额模式和交易动态。其次，我们构建了时间衰减可达性图和时间衰减交易模式图，旨在捕捉不同类型的时空比特币交易模式。第三，我们在两个图上都使用节点嵌入，并基于具有内置时间衰减因子的历史交易开发用户帐户之间的比特币交易预测系统。为了保持有效的交易预测性能，我们利用乘法模型更新 (MMU) 集成来组合基于从每个相应比特币交易图中提取的不同交易特征构建的预测模型。对真实世界的比特币交易数据进行评估，结果表明，我们的时空预测模型运行速度快，预测准确率超过 60%，与基于静态图基线的预测模型相比，预测性能提高了 50% . 为了保持有效的交易预测性能，我们利用乘法模型更新 (MMU) 集成来组合基于从每个相应比特币交易图中提取的不同交易特征构建的预测模型。对真实世界的比特币交易数据进行评估，结果表明，我们的时空预测模型运行速度快，预测准确率超过 60%，与基于静态图基线的预测模型相比，预测性能提高了 50% . 为了保持有效的交易预测性能，我们利用乘法模型更新 (MMU) 集成来组合基于从每个相应比特币交易图中提取的不同交易特征构建的预测模型。对真实世界的比特币交易数据进行评估，结果表明，我们的时空预测模型运行速度快，预测准确率超过 60%，与基于静态图基线的预测模型相比，预测性能提高了 50% .