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A Prediction Model for Bitcoin Cryptocurrency Prices

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Blockchain Applications in the Smart Era

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Cryptocurrencies like Bitcoin are a contentious and difficult technological innovation in today’s financial system. With huge improvements in financial markets, machine learning and artificial intelligence aided trading have piqued interest in recent years. This study suggests a predicting model for blockchain Bitcoin cryptocurrency prices and its profitability trading strategies using machine learning algorithms (ICA-Firefly and SVMs). For the prediction analysis of Bitcoin cryptocurrency data, this study combines ICA-Firefly with SVM algorithms. The model was tested on a large dataset of 2194 samples, and its performance was analyzed in terms of evaluation metrics. In evaluation to the state of the art, the ICA-Firefly with L-SVM and Sigmoid SVM classification approach performs well on the Bitcoin sample dataset, with an accuracy of 95% and 97%, respectively. The ICA-Firefly with the SVM model can be adopted as a viable financial system sustainability management strategy.

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References

  1. Podgorelec, B., Turkanović, M., & Karakatič, S. (2019). A machine learning-based method for automated Blockchain transaction signing including personalized anomaly detection. Sensors, 20, 147. https://doi.org/10.3390/s20010147

    Article  Google Scholar 

  2. Żbikowski, K. (2016). Application of machine learning algorithms for bitcoin automated trading. Presented at the (2016). https://doi.org/10.1007/978-3-319-30315-4_14.

  3. Gulihar, P., & Gupta, B. B. (2019). A taxonomy of bitcoin security issues and defense mechanisms. In Machine learning for computer and cyber security (pp. 209–232). CRC Press. https://doi.org/10.1201/9780429504044-9. Taylor & Francis Group, [2019] | “A science publishers book”.

    Chapter  Google Scholar 

  4. Pabuçcu, H., Ongan, S., & Ongan, A. (2020). Forecasting the movements of bitcoin prices: An application of machine learning algorithms. Quantitative Finance and Economics, 4, 679–692. https://doi.org/10.3934/QFE.2020031

    Article  Google Scholar 

  5. Li, X., & Wang, C. A. (2017). The technology and economic determinants of cryptocurrency exchange rates: The case of bitcoin. Decision Support Systems, 95, 49–60. https://doi.org/10.1016/j.dss.2016.12.001

    Article  Google Scholar 

  6. Casino, F., Dasaklis, T. K., & Patsakis, C. (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and Informatics, 36, 55–81. https://doi.org/10.1016/j.tele.2018.11.006

    Article  Google Scholar 

  7. Arowolo, M. O., Adebiyi, M. O., Ariyo, A. A., & Okesola, O. J. (2021). A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree. TELKOMNIKA (Telecommunication Computing Electronics and Control), 19, 310. https://doi.org/10.12928/telkomnika.v19i1.16381

    Article  Google Scholar 

  8. Alabi, K.O., Abdulsalam, S.O., Ogundokun, R.O., Arowolo, M.O. (2021). Credit risk prediction in commercial Bank using Chi-Square with SVM-RBF. Presented at the (2021). https://doi.org/10.1007/978-3-030-69143-1_13.

  9. Jang, H., & Lee, J. (2018). An empirical study on modeling and prediction of bitcoin prices with Bayesian neural networks based on Blockchain information. IEEE Access, 6, 5427–5437. https://doi.org/10.1109/ACCESS.2017.2779181

    Article  Google Scholar 

  10. Gupta, A., Nain, H. (2021). Bitcoin price prediction using time series analysis and machine learning techniques. Presented at the (2021). https://doi.org/10.1007/978-981-15-7106-0_54.

  11. Ampountolas, A., Nyarko Nde, T., Date, P., & Constantinescu, C. (2021). A machine learning approach for micro-credit scoring. Risks, 9, 50. https://doi.org/10.3390/risks9030050

    Article  Google Scholar 

  12. Fan, M.-H., Chen, M.-Y., & Liao, E.-C. (2021). A deep learning approach for financial market prediction: Utilization of Google trends and keywords. Granular Computing, 6, 207–216. https://doi.org/10.1007/s41066-019-00181-7

    Article  Google Scholar 

  13. Gerlein, E. A., McGinnity, M., Belatreche, A., & Coleman, S. (2016). Evaluating machine learning classification for financial trading: An empirical approach. Expert Systems with Applications, 54, 193–207. https://doi.org/10.1016/j.eswa.2016.01.018

    Article  Google Scholar 

  14. Huang, J., Chai, J., & Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14, 13. https://doi.org/10.1186/s11782-020-00082-6

    Article  Google Scholar 

  15. Khalid Salman, M., & Abdu Ibrahim, A. (2020). Price prediction of different cryptocurrencies using technical trade indicators and machine learning. IOP Conference Series: Materials Science and Engineering, 928, 032007. https://doi.org/10.1088/1757-899X/928/3/032007

    Article  Google Scholar 

  16. Sebastião, H., & Godinho, P. (2021). Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financial Innovation, 7, 3. https://doi.org/10.1186/s40854-020-00217-x

    Article  Google Scholar 

  17. Zhengyang, W., Xingzhou, L., Jinjin, R., & Jiaqing, K. (2019). Prediction of cryptocurrency price dynamics with multiple machine learning techniques. In Proceedings of the 2019 4th International Conference on Machine Learning Technologies – ICMLT 2019 (pp. 15–19). ACM Press. https://doi.org/10.1145/3340997.3341008

    Chapter  Google Scholar 

  18. Koker, T. E., & Koutmos, D. (2020). Cryptocurrency trading using machine learning. Journal of Risk and Financial Management, 13, 178. https://doi.org/10.3390/jrfm13080178

    Article  Google Scholar 

  19. Cho, H., Lee, K.-H., Kim, C. (2021). Machine learning and cryptocurrency in the financial markets. Presented at the (2021). https://doi.org/10.1007/978-981-33-6137-9_13.

  20. Cocco, L., Tonelli, R., & Marchesi, M. (2021). Predictions of bitcoin prices through machine learning based frameworks. Computer Science – PeerJ, 7, e413. https://doi.org/10.7717/peerj-cs.413

    Article  Google Scholar 

  21. Alessandretti, L., ElBahrawy, A., Aiello, L. M., & Baronchelli, A. (2018). Anticipating cryptocurrency prices using machine learning. Complexity, 2018, 1–16. https://doi.org/10.1155/2018/8983590

    Article  Google Scholar 

  22. Lahmiri, S., & Bekiros, S. (2021). Deep learning forecasting in cryptocurrency high-frequency trading. Cognitive Computation, 13, 485–487. https://doi.org/10.1007/s12559-021-09841-w

    Article  Google Scholar 

  23. Jameel, F., Javaid, U., Khan, W. U., Aman, M. N., Pervaiz, H., & Jäntti, R. (2020). Reinforcement learning in Blockchain-enabled IIoT networks: A survey of recent advances and open challenges. Sustainability, 12, 5161. https://doi.org/10.3390/su12125161

    Article  Google Scholar 

  24. Tanwar, S., Bhatia, Q., Patel, P., Kumari, A., Singh, P. K., & Hong, W.-C. (2020). Machine learning adoption in Blockchain-based smart applications: The challenges, and a way forward. IEEE Access, 8, 474–488. https://doi.org/10.1109/ACCESS.2019.2961372

    Article  Google Scholar 

  25. Gupta, R., Tanwar, S., Al-Turjman, F., Italiya, P., Nauman, A., & Kim, S. W. (2020). Smart contract privacy protection using AI in cyber-physical systems: Tools, techniques and challenges. IEEE Access., 8, 24746–24772. https://doi.org/10.1109/ACCESS.2020.2970576

    Article  Google Scholar 

  26. Liu, Y., Yu, F. R., Li, X., Ji, H., & Leung, V. C. M. (2020). Blockchain and machine learning for communications and networking systems. IEEE Communication Surveys and Tutorials, 22, 1392–1431. https://doi.org/10.1109/COMST.2020.2975911

    Article  Google Scholar 

  27. Shahbazi, Z., & Byun, Y.-C. (2021). Integration of Blockchain, IoT and machine learning for multistage quality control and enhancing security in smart manufacturing. Sensors, 21, 1467. https://doi.org/10.3390/s21041467

    Article  Google Scholar 

  28. Jaquart, P., Dann, D., & Weinhardt, C. (2021). Short-term bitcoin market prediction via machine learning. The Journal of Financial Data Science, 7, 45–66. https://doi.org/10.1016/j.jfds.2021.03.001

    Article  Google Scholar 

  29. Sharma, M. P., Bhardwaj, A. V. V., Sharma, V., Iqbal, A. P., & Kumar, R. (2020). Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system. Computers and Electrical Engineering, 81, 106527. https://doi.org/10.1016/j.compeleceng.2019.106527

    Article  Google Scholar 

  30. Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019). DeepChain: Auditable and privacy-preserving deep learning with Blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing, 1–1. https://doi.org/10.1109/TDSC.2019.2952332

  31. Negar, M., Alireza, N., Masoud, R., & Yasser, Z. (2020). Bitcoin price prediction based on other cryptocurrencies using machine learning and time series analysis. Iranian Journal of Science and Technology. https://doi.org/10.24200/SCI.2020.55034.4040

  32. Yahoo, F. Bitcoin price prediction, https://in.finance.yahoo.com/quote/BTC-USD/history?p=BTC-USD&guce_referrer=aHR0cHM6Ly9naXRodWIuY29tL3N1cmFqYmFoYW R1cjU5MS9CaXRjb2luLVByaWNlLVByZWRpY3Rpb24tVXNpbmctUk5OLS0tTFNUTQ &guce_referrer_sig=AQAAAHL6O0g7rnrvKBzzqIlA6B4fdHCk0Vp_mdcZPKO9V7W9d0d6 cMkBLA1DxSSlcvb49Oq_m9nOh9y-Ay6s73nEk31hxma615IMw9okiq3sqeK9L9rUxlqvPM rhvOjARP7X96ckdYbjO0HzbsPe9VXZXn4YqkOZe-dkS-dia7qDMB-3&guccounter=2.

  33. Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020). Analysis of dimensionality reduction techniques on big data. IEEE Access, 8, 54776–54788. https://doi.org/10.1109/ACCESS.2020.2980942

    Article  Google Scholar 

  34. Awotunde, J.B., Ogundokun, R.O., Jimoh, R.G., Misra, S., Aro, T.O. (2021). Machine learning algorithm for cryptocurrencies price prediction. Presented at the (2021). https://doi.org/10.1007/978-3-030-72236-4_17.

  35. Vaddi, L. (2020). Predicting crypto currency prices using machine learning and deep learning techniques. International Journal of Advanced Trends in Computer Science and Engineering, 9, 6603–6608. https://doi.org/10.30534/ijatcse/2020/351942020

    Article  Google Scholar 

  36. Tharwat, A. (2021). Independent component analysis: An introduction. Applied Computing and Informatics, 17, 222–249. https://doi.org/10.1016/j.aci.2018.08.006

    Article  Google Scholar 

  37. Maghrebi, H., Prouff, E. (2018). On the use of independent component analysis to denoise side-channel measurements. Presented at the (2018). https://doi.org/10.1007/978-3-319-89641-0_4.

  38. Gadekallu, T. R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P. K. R., Ra, I.-H., & Alazab, M. (2020). Early detection of diabetic retinopathy using PCA-firefly based deep learning model. Electronics, 9, 274. https://doi.org/10.3390/electronics9020274

    Article  Google Scholar 

  39. Veysel, A., Ahmet, N., & T., Farah, Hatem, K., Bashar, Ahmed, K. (2020). Wrapper feature selection approach based on binary firefly algorithm for spam E-mail filtering. Journal of Soft Computing and Data Mining, 1, 44–52. https://doi.org/10.30880/jscdm.2020.01.02.005

    Article  Google Scholar 

  40. Ferdiansyah, F., Negara, E. S., & Widyanti, Y. (2019). Bitcoin-USD trading using SVM to detect the current DAY’S trend in the market. Journal of Information Systems and Informatics, 1, 70–77. https://doi.org/10.33557/journal-isi.v1i1.7

    Article  Google Scholar 

  41. Ali Alahmari, S. (2020). PREDICTING THE PRICE OF CRYPTOCURRENCY USING SUPPORT VECTOR REGRESSION METHODS. J. Mech. Contin. The Mathematical Scientist, 15. https://doi.org/10.26782/jmcms.2020.04.00023

  42. Majeed, Y., Zhang, S., & Ren. (2021). A big data-driven framework for sustainable and smart additive manufacturing. Robotics and Computer-Integrated Manufacturing, 67, 102026. https://doi.org/10.1016/j.rcim.2020.102026

    Article  Google Scholar 

  43. Mudassir, M., Bennbaia, S., Unal, D., & Hammoudeh, M. (2020). Time-series forecasting of bitcoin prices using high-dimensional features: A machine learning approach. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-05129-6

  44. Li, Y., & Dai, W. (2020). Bitcoin price forecasting method based on CNN-LSTM hybrid neural network model. Journal of Engineering, 2020, 344–347. https://doi.org/10.1049/joe.2019.1203

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Landmark University, Omu-Aran, Nigeria

    Micheal Olaolu Arowolo & Peace Ayegba

  2. Department of Computer Science, Kwara State University, Malete, Nigeria

    Shakirat Ronke Yusuff

  3. Department of Computer Science and Communication,  Østfold University College (HIOF), Halden, Norway

    Sanjay Misra

Authors
  1. Micheal Olaolu Arowolo
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  2. Peace Ayegba
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  3. Shakirat Ronke Yusuff
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  4. Sanjay Misra
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Correspondence to Micheal Olaolu Arowolo .

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Editors and Affiliations

  1. Department of Computer Science and Communication,  Østfold University College (HIOF), Halden, Norway

    Sanjay Misra

  2. Vellore Institute of Technology, Tamil Nandu, India

    Amit Kumar Tyagi

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Arowolo, M.O., Ayegba, P., Yusuff, S.R., Misra, S. (2022). A Prediction Model for Bitcoin Cryptocurrency Prices. In: Misra, S., Kumar Tyagi, A. (eds) Blockchain Applications in the Smart Era. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-89546-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-89546-4_7

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