Enhancing bitcoin transaction confirmation prediction: a hybrid model combining neural networks and XGBoost

World Wide Web (2023) 26:4173–4191 https://doi.org/10.1007/s11280-023-01212-9 Enhancing bitcoin transaction confirmation prediction: a hybrid model combining neural networks and XGBoost Limeng Zhang1 · Rui Zhou1 · Qing Liu2 · Jiajie Xu3 · Chengfei Liu1 · Muhammad Ali Babar4 Received: 30 April 2023 / Revised: 12 September 2023 / Accepted: 14 September 2023 / Published online: 26 December 2023 © The Author(s) 2023 Abstract With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction’s confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network (Hybrid-CTEN), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed. Keywords Transaction confirmation time · Bitcoin · Blockchain · XGBoost · Neural network 1 Introduction As Bitcoin is universally recognized by more organisations, institutes and governments, it is booming in an increasing number of areas [1]. Currently, many businesses, such as PayPal, This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering 2022 Guest editors: Richard Chbeir, Helen Huang, Yannis Manolopoulos and Fabrizio Silvestri. B Rui Zhou Extended author information available on the last page of the article 123 4174 World Wide Web (2023) 26:4173–4191 Microsoft, and Overstock, have embraced Bitcoin as a method of payment. Meanwhile, various online cryptocurrency trading platforms, such as Coinbase, Gemini1 , and PayPal, have enabled users to purchase, sell, store, and transfer Bitcoins. As a result, more Bitcoin exchanges are expected to be populated into the Bitcoin blockchain. Unfortunately, due to the confirmation mechanism in the system, only a limited number of transactions (restricted to the capacity of a block) can be confirmed at a time. Therefore, many transactions cannot be immediately confirmed, and confirmation delays commonly occur in the Bitcoin system. Concerned about this, it becomes vital to help a user to understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. Most previous attempts at estimating the confirmation time for a transaction focus on predicting a specific timestamp or predicting the number of blocks a transaction needs to wait for before it is confirmed [2–9]. However, it is usually more practical to predict the confirmation time as falling into the corresponding predefined time intervals (e.g., within 1 hour, between 1 hour and 4 hours, and more than 4 hours). It is motivated by the following considerations: On one hand, when attempting to estimate a specific timestamp, one issue is that the estimation performance can be affected by the submission time, especially for transactions that are scheduled for confirmation in the subsequent block. The confirmation time for these transactions is influenced by the remaining time before the next block is produced. Consequently, this can lead to a situation where, as a result of delayed submission, a transaction with a significantly higher fee can experience a longer delay than a transaction with a lower fee if the higher fee one is submitted later than the lower-fee one. The second issue arises from the unpredictable nature of block generation time, which can span from mere seconds to several hundred seconds. As a result, the confirmation time for two transactions submitted at different block heights but confirmed within the same block interval can exhibit unpredictable differences, which may undermine users’ satisfaction when using a client-side transaction system. On the other hand, by utilizing the block as the unit of measurement for confirmation time, the variance in confirmation time can be significantly diminished. However, a challenge arises as the estimation result can be heavily influenced by a small proportion of transactions, especially when there is a scarcity of historical transactions for that interval. In such cases, the estimation result may become highly dependent on a single or a few transactions. Moreover, when the estimated confirmation time (in terms of both a specific time and a block interval) exceeds a certain level, users tend to pay a higher transaction fee to prioritize the confirmation process. In conclusion, we suggest that as long as the confirmation time falls within an acceptable range, it may be more practical and reasonable to estimate a confirmation time range rather than a confirmation time stamp to system users. Under such background, if we divide the future into a number of block intervals (representing a number of classes), the confirmation time prediction problem can be considered as a classification problem. The accuracy of transaction confirmation time estimation is crucial for blockchain-based applications. However, existing efforts suffer from four key drawbacks in their frameworks: (1) The existing methods for transaction confirmation estimation do not provide tailored estimates for individual transactions. Instead, most of them estimate the confirmation time for a group of transactions. For example, some works such as [5, 6] estimate the average confirmation time of high-feerate class transactions and low-feerate transactions, while others like [8] estimate the average confirmation time of all the unconfirmed transactions. (2) Models proposed in [3, 10] predict only whether a transaction can be confirmed in the next block, treating the problem as a binary classification task. However, such models may not be 1 https://www.gemini.com 123 World Wide Web (2023) 26:4173–4191 4175 sufficient in practice as they do not provide more detailed confirmation information beyond a simple yes or no. (3) Some of t (...truncated)