Skip to main content
SpringerLink
Log in

A new method of ensemble learning: case of cryptocurrency price prediction

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

This work proposes a novel method of ensemble learning for time series prediction. Different machine learning-based models have been integrated, and a combined prediction model has been created. The objective of the ensemble model is that it must outperform all other individual models that are used to construct the ensemble model in terms of producing excellent predictions. The field of cryptocurrencies has been selected as the domain of this work where the focus is to predict the cryptocurrency prices using the proposed model. A new regression model is proposed and implemented in this work. Different machine learning techniques have been adopted and integrated to form a combined prediction model. The machine learning models include deep neural networks, support vector regression, and decision trees. The regression scheme has to be implemented on each machine learning model separately as well as their performance is also to be improved. The combined prediction model requires optimal weights generation for integration, and therefore, time complexity is a concern. A large set of experiments have been carried out on various cryptocurrencies and the results are displayed. Real-world data has been used here and a comparison is also performed. It is observed that the combined prediction model outperforms other models resulting in excellent predictions capturing most of the nonstationary movements in the data.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Alonso-Monsalve S, Suárez-Cetrulo AL, Cervantes A, Quintana D (2020) Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Syst Appl 149:113250. https://doi.org/10.1016/J.ESWA.2020.113250

    Article  Google Scholar 

  2. Bartholomew DJ, Box GEP, Jenkins GM (1971) Time Series Analysis Forecasting and Control. Operat Res Q (1970-1977) 22(2):199. https://doi.org/10.2307/3008255

    Article  Google Scholar 

  3. Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econom 31(3):307–327. https://doi.org/10.1016/0304-4076(86)90063-1

    Article  MathSciNet  MATH  Google Scholar 

  4. Bouri E, Shahzad SJH, Roubaud D (2019) Co-explosivity in the cryptocurrency market. Financ Res Lett 29:178–183. https://doi.org/10.1016/J.FRL.2018.07.005

    Article  Google Scholar 

  5. Brown, R. G. (2004). Smoothing, Forecasting and Prediction of Discrete Time Series (Phoenix Edition). https://woqylenipo.leftebook.icu/smoothing-forecasting-and-prediction-of-discrete-time-series-book-8208wv.php

  6. Burges CJC. (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov 2(2):121–167. https://doi.org/10.1023/A:1009715923555

    Article  Google Scholar 

  7. Caporale GM, Gil-Alana L, Plastun A (2018) Persistence in the cryptocurrency market. Res Int Bus Financ 46:141–148. https://doi.org/10.1016/J.RIBAF.2018.01.002

    Article  Google Scholar 

  8. Corbet S, Meegan A, Larkin C, Lucey B, Yarovaya L (2018) Exploring the dynamic relationships between cryptocurrencies and other financial assets. Econ Lett 165:28–34. https://doi.org/10.1016/J.ECONLET.2018.01.004

    Article  Google Scholar 

  9. Cui S, Wang Y, Wang D, Sai Q, Huang Z, Cheng TCE (2021) A two-layer nested heterogeneous ensemble learning predictive method for COVID-19 mortality. Appl Soft Comput 113:107946. https://doi.org/10.1016/J.ASOC.2021.107946

    Article  Google Scholar 

  10. David SA, Inacio CMC, Nunes R, Machado JAT (2021) Fractional and fractal processes applied to cryptocurrencies price series. J Adv Res 32:85–98. https://doi.org/10.1016/J.JARE.2020.12.012

    Article  Google Scholar 

  11. de Albuquerque BS, de Castro Callado M (2015) Understanding Bitcoins: Facts and Questions. Rev Bras Econ 69(1):3–16. https://doi.org/10.5935/0034-7140.20150001

    Article  Google Scholar 

  12. Engle RF (1982) Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica 50(4):987. https://doi.org/10.2307/1912773

    Article  MathSciNet  MATH  Google Scholar 

  13. Extance A (2015) The future of cryptocurrencies: Bitcoin and beyond. Nature 526(7571):21–23. https://doi.org/10.1038/526021A

    Article  Google Scholar 

  14. Frankovic J, Liu B, Suardi S (2021) On spillover effects between cryptocurrency-linked stocks and the cryptocurrency market: Evidence from Australia. Glob Financ J. https://doi.org/10.1016/J.GFJ.2021.100642

    Article  Google Scholar 

  15. Freitas FD, de Souza AF, de Almeida AR (2009) Prediction-based portfolio optimization model using neural networks. Neurocomputing 72(10–12):2155–2170. https://doi.org/10.1016/J.NEUCOM.2008.08.019

    Article  Google Scholar 

  16. Gandal N, Halaburda H (2014) Competition in the Cryptocurrency Market. SSRN Electron J. https://doi.org/10.2139/SSRN.2506577

    Article  MATH  Google Scholar 

  17. Hamayel M J, Owda AY (2021) A novel cryptocurrency price prediction model using GRU, LSTM and bi-LSTM machine learning algorithms. AI 2(4):477–496. https://doi.org/10.3390/ai2040030

    Article  Google Scholar 

  18. Jalan A, Matkovskyy R, Yarovaya L (2021) “Shiny” crypto assets: A systemic look at gold-backed cryptocurrencies during the COVID-19 pandemic. Int Rev Financ Anal 78:101958. https://doi.org/10.1016/J.IRFA.2021.101958

    Article  Google Scholar 

  19. Jiménez I, Mora-Valencia A, Perote J (2022) Semi-nonparametric risk assessment with cryptocurrencies. Res Int Bus Financ 59:101567. https://doi.org/10.1016/J.RIBAF.2021.101567

    Article  Google Scholar 

  20. Ji Q, Bouri E, Lau CKM, Roubaud D (2019) Dynamic connectedness and integration in cryptocurrency markets. Int Rev Financ Anal 63:257–272. https://doi.org/10.1016/J.IRFA.2018.12.002

    Article  Google Scholar 

  21. Ji S, Kim J, Im H (2019) A comparative study of bitcoin price prediction using deep learning. Mathematics 7(10):898. https://doi.org/10.3390/math7100898

    Article  Google Scholar 

  22. Lauriola I, Gallicchio C, Aiolli F (2020) Enhancing deep neural networks via multiple kernel learning. Pattern Recogn 101:107194. https://doi.org/10.1016/J.PATCOG.2020.107194

    Article  Google Scholar 

  23. Nakamoto, S. (n.d.). Bitcoin: A Peer-to-Peer Electronic Cash System. Retrieved November 22, 2021, from www.bitcoin.org

  24. Nazir A, Mir RN, Qureshi S (2020) Exploring compression and parallelization techniques for distribution of deep neural networks over Edge-Fog continuum – a review. Inter J Intell Comput Cyber 13(3):331–364. https://doi.org/10.1108/IJICC-04-2020-0038/FULL/XML

    Article  Google Scholar 

  25. Pahič R, Ridge B, Gams A, Morimoto J, Ude A (2020) Training of deep neural networks for the generation of dynamic movement primitives. Neural Netw 127:121–131. https://doi.org/10.1016/J.NEUNET.2020.04.010

    Article  MATH  Google Scholar 

  26. Poongodi M, Sharma A, Vijayakumar V, Bhardwaj V, Sharma AP, Iqbal R, Kumar R (2020) Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system. Comput Electr Eng 81:106527. https://doi.org/10.1016/J.COMPELECENG.2019.106527

    Article  Google Scholar 

  27. Rather AM (2021) LSTM-based Deep Learning Model for Stock Prediction and Predictive Optimization Model. EURO J Decision Process 9:100001. https://doi.org/10.1016/J.EJDP.2021.100001

    Article  Google Scholar 

  28. Rather AM, Agarwal A, Sastry VN (2015) Recurrent neural network and a hybrid model for prediction of stock returns. Expert Syst Appl 42(6):3234–3241. https://doi.org/10.1016/J.ESWA.2014.12.003

    Article  Google Scholar 

  29. Samarasinghe, Sandhya. (2007). Neural networks for applied sciences and engineering : from fundamentals to complex pattern recognition. Auerbach. https://www.routledge.com/Neural-Networks-for-Applied-Sciences-and-Engineering-From-Fundamentals/Samarasinghe/p/book/9780849333750

  30. Shakri, IH (2021) Time series prediction using machine learning: a case of Bitcoin returns. Studies in Economics and Finance, ahead-of-print(ahead-of-print). https://doi.org/10.1108/SEF-06-2021-0217

  31. Shin SG, Jin S (1998) Optical neural network using fractional Fourier transform, log-likelihood, and parallelism. Opt Commun 153(4–6):218–222. https://doi.org/10.1016/S0030-4018(98)00231-4

    Article  Google Scholar 

  32. Simidjievski N, Todorovski L, Džeroski S (2015) Predicting long-term population dynamics with bagging and boosting of process-based models. Expert Syst Appl 42(22):8484–8496. https://doi.org/10.1016/J.ESWA.2015.07.004

    Article  Google Scholar 

  33. Syam N, Kaul R (2021) Support Vector Machines in Marketing and Sales. Mach Learn Artif Intell Market Sales. https://doi.org/10.1108/978-1-80043-880-420211005

    Article  Google Scholar 

  34. Tanwar S, Patel NP, Patel SN, Patel JR, Sharma G, Davidson IE (2021) Deep learning-based cryptocurrency price prediction scheme with inter-dependent relations. IEEE Access 9:138633–138646. https://doi.org/10.1109/ACCESS.2021.3117848

    Article  Google Scholar 

  35. Tschorsch F, Scheuermann B (2016) Bitcoin and beyond: A technical survey on decentralized digital currencies. IEEE Commun Surv Tutorials 18(3):2084–2123. https://doi.org/10.1109/COMST.2016.2535718

    Article  Google Scholar 

  36. Wang J, Liu L (2020) A multi-attention deep neural network model base on embedding and matrix factorization for recommendation. Inter J Cogn Comput Eng 1:70–77. https://doi.org/10.1016/J.IJCCE.2020.11.002

    Article  Google Scholar 

  37. Wang S, Zhang Y, Zhang C, Yang M (2020) Improved artificial neural network method for predicting photovoltaic output performance. Global Energy Interconnect 3(6):553–561. https://doi.org/10.1016/J.GLOEI.2021.01.005

    Article  Google Scholar 

  38. Wu J, Liu J, Zhao Y, Zheng Z (2021) Analysis of cryptocurrency transactions from a network perspective: An overview. J Netw Comput Appl 190:103139. https://doi.org/10.1016/J.JNCA.2021.103139

    Article  Google Scholar 

  39. Yan T, Shen S-L, Zhou A, Chen X-S (2022) Prediction of geological characteristics from shield operational parameters using integrating grid search and K-fold cross validation into stacking classification algorithm. J Rock Mech Geotechn Eng. https://doi.org/10.1016/J.JRMGE.2022.03.002

    Article  Google Scholar 

Download references

Acknowledgements

The author highly acknowledges the research support received from the Great Lakes Institute of Management, Gurgaon.

Author information

Authors and Affiliations

  1. Great Lakes Institute of Management, Gurgaon, 122413, Haryana, India

    Akhter Mohiuddin Rather

Authors
  1. Akhter Mohiuddin Rather
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

I am the sole author.

Corresponding author

Correspondence to Akhter Mohiuddin Rather.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rather, A.M. A new method of ensemble learning: case of cryptocurrency price prediction. Knowl Inf Syst 65, 1179–1197 (2023). https://doi.org/10.1007/s10115-022-01796-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-022-01796-0

Keywords

Search

Navigation