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dc.contributor.authorTedeschi, Enrico
dc.contributor.authorNordmo, Tor-Arne Schmidt
dc.contributor.authorJohansen, Dag
dc.contributor.authorJohansen, Håvard D.
dc.date.accessioned2020-04-01T08:37:14Z
dc.date.available2020-04-01T08:37:14Z
dc.date.issued2019
dc.description.abstractProof-of-work based cryptocurrencies, like Bitcoin, have a fee market where transactions are included in the blockchain according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience delays and evictions unless an enormous fee is paid. % In this paper, we present a novel machine-learning model, solving a binary classification problem, that can predict transaction fee volatility in the Bitcoin network so that users can optimize their fees expenses and the approval time for their transactions. % The model's output will give a confidence score whether a new incoming transaction will be included in the next mined block. The model is trained on data from a longitudinal study of the Bitcoin blockchain, containing more than 10 million transactions. New features that we generate include information on how many bytes were already occupied by other transactions in the mempool, assuming they are ordered by fee density in each mining pool. The collected dataset allows to generate a model for transaction inclusion pattern prediction in the Bitcoin network, hence telling whether a transaction is well formed or not, according to the previous transactions analyzed. With this, we obtain a prediction score for up to 86%.en_US
dc.identifier.citationTedesch,i E; Nordmo, TA; Johansen, D; Johansen, H.J. (2019) Predicting Transaction Latency with Deep Learning in Proof-of-Work Blockchains. <i> Proceedings of 2019 IEEE International Conference on Big Data (Big Data)</i>, 4223-4231.en_US
dc.identifier.cristinIDFRIDAID 1773511
dc.identifier.doi10.1109/BigData47090.2019.9006228
dc.identifier.issn2639-1589
dc.identifier.urihttps://hdl.handle.net/10037/17954
dc.language.isoengen_US
dc.publisherIEEE (Institute of Electrical and Electronical Engineers)en_US
dc.relation.ispartofTedeschi, E. (2023). Predictive Modeling for Fair and Efficient Transaction Inclusion in Proof-of-Work Blockchain Systems. (Doctoral thesis). <a href=https://hdl.handle.net/10037/31116>https://hdl.handle.net/10037/31116</a>.
dc.relation.journalTEMP 2017 IEEE International Conference on Big Data (Big Data)
dc.relation.projectIDNorges forskningsråd: 275516en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright © 2019, IEEEen_US
dc.subjectVDP::Matematikk og naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kommunikasjon og distribuerte systemer: 423en_US
dc.subjectVDP::Mathematics and natural scienses: 400::Information and communication science: 420::Communication and distributed systems: 423en_US
dc.subjectBlockchain Technology / Blockchain Technologyen_US
dc.titlePredicting Transaction Latency with Deep Learning in Proof-of-Work Blockchainsen_US
dc.type.versionacceptedVersionen_US
dc.typePeer revieweden_US
dc.typeChapteren_US


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