An Analysis of Transaction Handling in Bitcoin

Gebraselase, Befekadu G.; Helvik, Bjarne E.; Jiang, Yuming

doi:10.1109/smds53860.2021.00030

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…[Al-Shehabi, 2018] and [Singh and Hafid, 2020] discuss supervised learning algorithms for confirmation times in Bitcoin and Ethereum respectively. [Gebraselase et al, 2021] performs an elaborate time series analysis for exploration and prediction of various properties of Bitcoin, one of which is the confirmation time.…”

Section: Beyond the Stochastic Modelling Of Bitcoin Confirmation Timesmentioning

confidence: 99%

Predicting confirmation times of Bitcoin transactions

Gundlach

Gijsbers

Koops

et al. 2021

SIGMETRICS Perform. Eval. Rev.

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We study the distribution of confirmation times of Bitcoin transactions, conditional on the size of the current memory pool. We argue that the time until a Bitcoin transaction is confirmed resembles the time to ruin in a corresponding Cramer-Lundberg process. This well-studied model gives mathematical insights in the mempool behaviour over time. Specifically, for situations where one chooses a fee, such that the total size of incoming transactions with higher fee is close to the total size of transactions leaving the mempool (heavy traffic), a diffusion approximation leads to an inverse Gaussian distribution for the confirmation times. The results of this paper are particularly interesting for users that want to make a Bitcoin transaction during heavy-traffic situations, as evaluation of the well-known inverse Gaussian distribution is computationally straightforward.

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Section: Beyond the Stochastic Modelling Of Bitcoin Confirmation Timesmentioning

confidence: 99%