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Multivariate Bitcoin Price Prediction Based on Tuned Bidirectional Long Short-Term Memory Network and Enhanced Reptile Search Algorithm

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Information and Software Technologies (ICIST 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1979))

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Abstract

Cryptocurrency price prediction and investment is a popular and relevant area of business nowadays. It involves analyzing historical data to forecast future trends and movements in asset prices. Bitcoin has gained significant prominence in the worldwide financial market as an investment asset. However, the high volatility of its price has attracted considerable attention from researchers and investors alike, leading to a growing interest in understanding the factors that drive its movement. This paper builds upon a research and conducts an empirical approach into the time-series data of a diverse range of exogenous and endogenous variables. Specifically, in this paper, the closing prices of Bitcoin, Ethereum and the daily volume of Bitcoin-related tweets are examined. For forecasting closing Bitcoin price based on the above mentioned predictors, bidirectional long-short term memory (BiLSTM) network tuned by hybrid adaptive reptile search algorithm is proposed. The analysis covers a three-year period from January 2020 to August 2022 and employs a three-fold split of the data to train, validation, and testing datasets. The best generated model by algorithm introduced in this manuscript is compared to other BiLSTM networks tuned by other cutting-edge metaheuristics and achieved results revealed that the method introduced in this research outperformed all other competitors regarding standard regression metrics.

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References

  1. Abualigah, L., Abd Elaziz, M., Sumari, P., Geem, Z.W., Gandomi, A.H.: Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst. Appl. 191, 116158 (2022)

    Article  Google Scholar 

  2. Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., Gandomi, A.H.: The arithmetic optimization algorithm. Comput. Methods Appl. Mech. Eng. 376, 113609 (2021)

    Article  MathSciNet  Google Scholar 

  3. Alameer, Z., Elaziz, M.A., Ewees, A.A., Ye, H., Jianhua, Z.: Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm. Resour. Policy 61, 250–260 (2019)

    Article  Google Scholar 

  4. Alhnaity, B., Abbod, M.: A new hybrid financial time series prediction model. Eng. Appl. Artif. Intell. 95, 103873 (2020)

    Google Scholar 

  5. Aslam, N., Rustam, F., Lee, E., Washington, P.B., Ashraf, I.: Sentiment analysis and emotion detection on cryptocurrency related tweets using ensemble LSTM-GRU model. IEEE Access 10, 39313–39324 (2022)

    Article  Google Scholar 

  6. Bacanin, N., Sarac, M., Budimirovic, N., Zivkovic, M., AlZubi, A.A., Bashir, A.K.: Smart wireless health care system using graph LSTM pollution prediction and dragonfly node localization. Sustain. Comput. Inform. Syst. 35, 100711 (2022)

    Google Scholar 

  7. Bacanin, N., Sarac, M., Budimirovic, N., Zivkovic, M., AlZubi, A.A., Bashir, A.K.: Smart wireless health care system using graph LSTM pollution prediction and dragonfly node localization. Sustain. Comput. Inform. Syst. 35, 100711 (2022)

    Google Scholar 

  8. Bacanin, N., Stoean, C., Zivkovic, M., Rakic, M., Strulak-Wójcikiewicz, R., Stoean, R.: On the benefits of using metaheuristics in the hyperparameter tuning of deep learning models for energy load forecasting. Energies 16(3), 1434 (2023)

    Article  Google Scholar 

  9. Bacanin, N., Tuba, E., Zivkovic, M., Strumberger, I., Tuba, M.: Whale optimization algorithm with exploratory move for wireless sensor networks localization. In: Abraham, A., Shandilya, S.K., Garcia-Hernandez, L., Varela, M.L. (eds.) HIS 2019. AISC, vol. 1179, pp. 328–338. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-49336-3_33

    Chapter  Google Scholar 

  10. Bacanin, N., Zivkovic, M., Bezdan, T., Venkatachalam, K., Abouhawwash, M.: Modified firefly algorithm for workflow scheduling in cloud-edge environment. Neural Comput. Appl. 34(11), 9043–9068 (2022)

    Article  Google Scholar 

  11. Bacanin, N., Zivkovic, M., Jovanovic, L., Ivanovic, M., Rashid, T.A.: Training a multilayer perception for modeling stock price index predictions using modified whale optimization algorithm. In: Smys, S., Tavares, J.M.R.S., Balas, V.E. (eds.) Computational Vision and Bio-Inspired Computing. AISC, vol. 1420, pp. 415–430. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-9573-5_31

    Chapter  Google Scholar 

  12. Bezdan, T., Zivkovic, M., Bacanin, N., Strumberger, I., Tuba, E., Tuba, M.: Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm. J. Intell. Fuzzy Syst. 42(1), 411–423 (2022)

    Article  Google Scholar 

  13. Chen, Q., Zhang, W., Lou, Y.: Forecasting stock prices using a hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network. IEEE Access 8, 117365–117376 (2020)

    Article  Google Scholar 

  14. Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: algorithm and applications. Futur. Gener. Comput. Syst. 97, 849–872 (2019)

    Article  Google Scholar 

  15. Hitam, N.A., Ismail, A.R., Saeed, F.: An optimized support vector machine (SVM) based on particle swarm optimization (PSO) for cryptocurrency forecasting. Procedia Comput. Sci. 163, 427–433 (2019)

    Article  Google Scholar 

  16. Huang, X., et al.: LSTM based sentiment analysis for cryptocurrency prediction. In: Jensen, C.S., et al. (eds.) DASFAA 2021. LNCS, vol. 12683, pp. 617–621. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73200-4_47

    Chapter  Google Scholar 

  17. Jovanovic, D., Antonijevic, M., Stankovic, M., Zivkovic, M., Tanaskovic, M., Bacanin, N.: Tuning machine learning models using a group search firefly algorithm for credit card fraud detection. Mathematics 10(13) (2022). https://www.mdpi.com/2227-7390/10/13/2272

  18. Jovanovic, L., et al.: Multi-step crude oil price prediction based on LSTM approach tuned by salp swarm algorithm with disputation operator. Sustainability 14(21), 14616 (2022)

    Article  Google Scholar 

  19. Jovanovic, L., Zivkovic, M., Antonijevic, M., Jovanovic, D., Ivanovic, M., Jassim, H.S.: An emperor penguin optimizer application for medical diagnostics. In: 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC), pp. 191–196. IEEE (2022)

    Google Scholar 

  20. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)

    Article  Google Scholar 

  21. Khedr, A.M., Arif, I., El-Bannany, M., Alhashmi, S.M., Sreedharan, M.: Cryptocurrency price prediction using traditional statistical and machine-learning techniques: a survey. Intell. Syst. Account. Financ. Manag. 28(1), 3–34 (2021)

    Article  Google Scholar 

  22. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)

    Article  Google Scholar 

  23. Mohapatra, S., Ahmed, N., Alencar, P.: Kryptooracle: a real-time cryptocurrency price prediction platform using twitter sentiments, pp. 5544–5551 (2019)

    Google Scholar 

  24. Park, H.W., Lee, Y.: How are twitter activities related to top cryptocurrencies’ performance? Evidence from social media network and sentiment analysis. Drustvena istrazivanja 28, 435–460 (2019)

    Article  Google Scholar 

  25. Patel, M.M., Tanwar, S., Gupta, R., Kumar, N.: A deep learning-based cryptocurrency price prediction scheme for financial institutions. J. Inf. Secur. Appl. 55, 102583 (2020)

    Google Scholar 

  26. Prakash, S., Kumar, M.V., Ram, S.R., Zivkovic, M., Bacanin, N., Antonijevic, M.: Hybrid GLFIL enhancement and encoder animal migration classification for breast cancer detection. Comput. Syst. Sci. Eng. 41(2), 735–749 (2022)

    Article  Google Scholar 

  27. Q. Chen, W.Z., Lou, Y.: Forecasting stock prices using a hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network. IEEE Access 8, 117365–117376 (2020)

    Google Scholar 

  28. Thakkar, A., Chaudhari, K.: A comprehensive survey on portfolio optimization, stock price and trend prediction using particle swarm optimization. Arch. Comput. Methods 28, 2133–2164 (2021)

    Article  MathSciNet  Google Scholar 

  29. Wang, D., Tan, D., Liu, L.: Particle swarm optimization algorithm: an overview. Soft Comput. 22, 387–408 (2018)

    Article  Google Scholar 

  30. Yan, H., Ouyang, H.: Financial time series prediction based on deep learning. Wirel. Pers. Commun. 102, 683–700 (2018)

    Article  Google Scholar 

  31. Yang, X.S., Slowik, A.: Firefly algorithm. In: Swarm Intelligence Algorithms, pp. 163–174. CRC Press (2020)

    Google Scholar 

  32. Zivkovic, M., et al.: COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain. Urban Areas 66, 102669 (2021)

    Google Scholar 

  33. Zivkovic, M., Petrovic, A., Venkatachalam, K., Strumberger, I., Jassim, H.S., Bacanin, N.: Novel chaotic best firefly algorithm: COVID-19 fake news detection application. In: Biswas, A., Kalayci, C.B., Mirjalili, S. (eds.) Advances in Swarm Intelligence. Studies in Computational Intelligence, vol. 1054, pp. 285–305. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-09835-2_16

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Authors and Affiliations

  1. Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

    Ivana Strumberger, Miodrag Zivkovic, Aleksandar Djordjevic, Jelena Gajic & Nebojsa Bacanin

  2. Modern College of Business and Science, Muscat, Oman

    Venkat Ram Raj Thumiki

Authors
  1. Ivana Strumberger
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  2. Miodrag Zivkovic
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  3. Venkat Ram Raj Thumiki
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  4. Aleksandar Djordjevic
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  5. Jelena Gajic
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  6. Nebojsa Bacanin
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Correspondence to Nebojsa Bacanin .

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Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Audrius Lopata

  2. Kaunas University of Technology, Kaunas, Lithuania

    Daina Gudonienė

  3. Kaunas University of Technology, Kaunas, Lithuania

    Rita Butkienė

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Strumberger, I., Zivkovic, M., Thumiki, V.R.R., Djordjevic, A., Gajic, J., Bacanin, N. (2024). Multivariate Bitcoin Price Prediction Based on Tuned Bidirectional Long Short-Term Memory Network and Enhanced Reptile Search Algorithm. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2023. Communications in Computer and Information Science, vol 1979. Springer, Cham. https://doi.org/10.1007/978-3-031-48981-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-48981-5_4

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  • Print ISBN: 978-3-031-48980-8

  • Online ISBN: 978-3-031-48981-5

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