  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.
