In the Bitcoin system, transaction fees serve as an incentive for blockchain confirmations. In general, a transaction with a higher fee is likely to be included in the next block mined, whereas a transaction with a smaller fee or no fee may be delayed or never processed at all. However, the transaction fee needs to be specified when submitting a transaction and almost cannot be altered thereafter. Hence it is indispensable to help a client set a reasonable fee, as a higher fee incurs over-spending and a lower fee could delay the confirmation. In this work, we focus on estimating the transaction fee for a new transaction to help with its confirmation within a given expected time. We identify two major drawbacks in the existing works. First, the current industry products are built on explicit analytical models, ignoring the complex interactions of different factors which could be better captured by machine learning based methods; Second, all of the existing works utilize limited knowledge for the estimation which hinders the potential of further improving the estimation quality. As a result, we propose a framework FENN, which aims to integrate the knowledge from a wide range of sources, including the transaction itself, unconfirmed transactions in the mempool and the blockchain confirmation environment, into a neural network model in order to estimate a proper transaction fee. Finally, we conduct experiments on real blockchain datasets to demonstrate the effectiveness and efficiency of our proposed framework over the state-of-the-art works evaluated by MAPE and RMSE. Each variation model in our framework can finish training within one block interval, which shows the potential of our framework to process the realtime transaction updates in the Bitcoin blockchain.

翻译：在比特币系统中，交易费用作为区块链确认的激励机制。一般而言，具有较高费用的交易更可能被纳入下一个挖出的区块中，而费用较低或无费用的交易则可能被延迟甚至永远无法处理。然而，交易费用需在提交交易时指定，此后几乎无法更改。因此，帮助用户设定合理费用至关重要——过高的费用会导致资金浪费，而过低的费用则会延迟确认。本研究聚焦于为新交易估算交易费用，以帮助其在预期时间内获得确认。我们指出现有研究存在两大缺陷：首先，当前行业产品基于显式分析模型构建，忽略了不同因素间复杂的相互作用，而基于机器学习的方法能更好地捕捉这些关系；其次，现有研究均利用有限知识进行估算，制约了进一步提升估算质量的潜力。为此，我们提出FENN框架，旨在将交易自身信息、内存池中未确认交易及区块链确认环境等多源知识整合到神经网络模型中，以实现合理的交易费用估算。最后，我们在真实区块链数据集上进行实验，通过MAPE和RMSE指标证明所提框架相较于前沿方法具有更高的效能与效率。框架中各变体模型均能在一个区块间隔内完成训练，这表明我们的框架具备处理比特币区块链实时交易更新的潜力。