Skip to main content
SpringerLink
Log in

Bitcoin Transaction Confirmation Time Prediction: A Classification View

  • Conference paper
  • First Online:
Web Information Systems Engineering – WISE 2022 (WISE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13724))

Included in the following conference series:

Abstract

With Bitcoin being universally recognised 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. One of the most demanding requirements for users is to estimate the confirmation time of a newly submitted transaction. In this paper, we argue that it is more practical to predict the confirmation time as falling into a time interval rather than falling onto a specific timestamp. After dividing the future into a set of time intervals (i.e. classes), the prediction of a transaction’s confirmation can be considered as a classification problem. Consequently, a number of mainstream classification methods, including neural networks and ensemble learning models, are evaluated. For comparison, we also design a baseline classifier that considers only the transaction feerate. Experiments on real-world blockchain data demonstrate that ensemble learning models can obtain higher accuracy, while neural network models perform better on the f1-score, especially when more classes are used.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.coindesk.com/price/bitcoin.

  2. 2.

    Blockchain.com, https://www.blockchain.com/charts/transactions-per-second.

  3. 3.

    Segwit transactions relocate the unlocking script (witness) from within the transaction to an external data structure, resulting a smaller size in terms of its raw data.

  4. 4.

    https://www.blockchain.com/api/blockchain_api.

References

  1. Antonopoulos, A.M.: Mastering Bitcoin: Programming the Open Blockchain. O’Reilly Media, Inc., Sebastopol (2017)

    Google Scholar 

  2. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

  3. Balsamo, S., Marin, A., Mitrani, I., Rebagliati, N.: Prediction of the consolidation delay in blockchain-based applications. In: Proceedings of the ACM/SPEC International Conference on Performance Engineering, pp. 81–92 (2021)

    Google Scholar 

  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  5. Chen, T., et al.: XGBoost: extreme gradient boosting. R Package Version 0.4-2 1(4), 1–4 (2015)

    Google Scholar 

  6. Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., Lehmann, S.: Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. arXiv preprint arXiv:1708.00524 (2017)

  7. Fiz, B., Hommes, S., State, R.: Confirmation delay prediction of transactions in the bitcoin network. In: Park, J.J., Loia, V., Yi, G., Sung, Y. (eds.) CUTE/CSA -2017. LNEE, vol. 474, pp. 534–539. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7605-3_88

    Chapter  Google Scholar 

  8. Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324–328. IEEE (2016)

    Google Scholar 

  9. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)

    Article  MATH  Google Scholar 

  10. Gundlach, R., Gijsbers, M., Koops, D., Resing, J.: Predicting confirmation times of bitcoin transactions. ACM SIGMETRICS Perform. Eval. Rev. 48(4), 16–19 (2021)

    Article  Google Scholar 

  11. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  12. Kasahara, S., Kawahara, J.: Effect of bitcoin fee on transaction-confirmation process. J. Ind. Manag. Optim. 15(1), 365 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  13. Kawase, Y., Kasahara, S.: Priority queueing analysis of transaction-confirmation time for bitcoin. J. Ind. Manag. Optim. 16(3), 1077 (2020)

    Article  MathSciNet  Google Scholar 

  14. Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  15. Kleinrock, L.: Theory, vol. 1. Queueing Systems (1975)

    Google Scholar 

  16. Ko, K., Jeong, T., Maharjan, S., Lee, C., Hong, J.W.-K.: Prediction of bitcoin transactions included in the next block. In: Zheng, Z., Dai, H.-N., Tang, M., Chen, X. (eds.) BlockSys 2019. CCIS, vol. 1156, pp. 591–597. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-2777-7_48

    Chapter  Google Scholar 

  17. Kontschieder, P., Fiterau, M., Criminisi, A., Bulo, S.R.: Deep neural decision forests. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1467–1475 (2015)

    Google Scholar 

  18. Koops, D.: Predicting the confirmation time of bitcoin transactions. arXiv preprint arXiv:1809.10596 (2018)

  19. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  20. Ma, C., Liu, Z., Cao, Z., Song, W., Zhang, J., Zeng, W.: Cost-sensitive deep forest for price prediction. Pattern Recogn. 107, 107499 (2020)

    Article  Google Scholar 

  21. Ma, Y., Sun, Y., Lei, Y., Qin, N., Lu, J.: A survey of blockchain technology on security, privacy, and trust in crowdsourcing services. World Wide Web 23(1), 393–419 (2020)

    Article  Google Scholar 

  22. McNally, S., Roche, J., Caton, S.: Predicting the price of bitcoin using machine learning. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 339–343. IEEE (2018)

    Google Scholar 

  23. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. In: Decentralized Business Review, p. 21260 (2008)

    Google Scholar 

  24. Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619–1630 (2006)

    Article  Google Scholar 

  25. Sagi, O., Rokach, L.: Ensemble learning: a survey. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 8(4), e1249 (2018)

    Google Scholar 

  26. Srivastava, N., Mansimov, E., Salakhudinov, R.: Unsupervised learning of video representations using LSTMs. In: International Conference on Machine Learning, pp. 843–852 (2015)

    Google Scholar 

  27. Su, R., Liu, X., Wei, L., Zou, Q.: Deep-Resp-Forest: a deep forest model to predict anti-cancer drug response. Methods 166, 91–102 (2019)

    Article  Google Scholar 

  28. Sun, L., et al.: Adaptive feature selection guided deep forest for COVID-19 classification with chest CT. IEEE J. Biomed. Health Inform. 24(10), 2798–2805 (2020)

    Article  Google Scholar 

  29. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  30. Wen, G., Hou, Z., Li, H., Li, D., Jiang, L., Xun, E.: Ensemble of deep neural networks with probability-based fusion for facial expression recognition. Cogn. Comput. 9(5), 597–610 (2017)

    Article  Google Scholar 

  31. Zhang, G.P.: Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 30(4), 451–462 (2000)

    Google Scholar 

  32. Zhang, L., Zhou, R., Liu, Q., Xu, J., Liu, C.: Transaction confirmation time estimation in the bitcoin blockchain. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds.) WISE 2021. LNCS, vol. 13080, pp. 30–45. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90888-1_3

    Chapter  Google Scholar 

  33. Zhao, W., Jin, S., Yue, W.: Analysis of the average confirmation time of transactions in a blockchain system. In: Phung-Duc, T., Kasahara, S., Wittevrongel, S. (eds.) QTNA 2019. LNCS, vol. 11688, pp. 379–388. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27181-7_23

    Chapter  Google Scholar 

  34. Zhong, L., Hu, L., Zhou, H.: Deep learning based multi-temporal crop classification. Remote Sens. Environ. 221, 430–443 (2019)

    Article  Google Scholar 

  35. Zhou, T., Sun, X., Xia, X., Li, B., Chen, X.: Improving defect prediction with deep forest. Inf. Softw. Technol. 114, 204–216 (2019)

    Article  Google Scholar 

  36. Zhou, Z.H., Feng, J.: Deep forest: towards an alternative to deep neural networks. In: IJCAI (2017)

    Google Scholar 

Download references

Acknowledgements

This research is supported by Data61, Australian Research Council Discover (Grant No. DP170104747, No. DP180100212 and No. DP200103700) and National Natural Science Foundation of China (Grant No. 61872258).

Author information

Authors and Affiliations

  1. Swinburne University of Technology, Melbourne, Australia

    Limeng Zhang, Rui Zhou & Chengfei Liu

  2. Data61, CSIRO, Hobart, Australia

    Limeng Zhang & Qing Liu

  3. Soochow University, Suzhou, China

    Jiajie Xu

Authors
  1. Limeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiajie Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chengfei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rui Zhou .

Editor information

Editors and Affiliations

  1. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

  2. The University of Queensland, Brisbane, QLD, Australia

    Helen Huang

  3. Sapienza Università di Roma, Rome, Italy

    Fabrizio Silvestri

  4. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  5. The New Cyber Research Department, Peng Cheng Laboratory, Shenzhen, China

    Yanchun Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, L., Zhou, R., Liu, Q., Xu, J., Liu, C. (2022). Bitcoin Transaction Confirmation Time Prediction: A Classification View. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20891-1_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20890-4

  • Online ISBN: 978-3-031-20891-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation