Metaheuristic Assisted Hybrid Classifier for Bitcoin Price Prediction

References

• Aggarwal, D., S. Chandrasekaran, and B. Annamalai. 2020. A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices. Journal of Behavioral and Experimental Finance 27:100335. doi:10.1016/j.jbef.2020.100335.
   Web of Science ®Google Scholar
• ATR. 2020. Average true range (ATR). https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/average-true-range-atr/ (accessed June 5, 2020).
   Google Scholar
• Atsalakis, G. S., I. G. Atsalaki, F. Pasiouras, and C. Zopounidis. 2019. Bitcoin price forecasting with neuro-fuzzy techniques. European Journal of Operational Research 276 (2):770–80. doi:10.1016/j.ejor.2019.01.040.
   Web of Science ®Google Scholar
• Chambers, C. 2020. Money + markets: While traders react emotionally to the spread of the virus, causing price volatility, Bitcoin is the new digital equivalent of gold. Engineering & Technology 15 (2):13. doi:10.1049/et.2020.0218.
   Google Scholar
• Chen, W., H. Xu, L. Jia, and Y. Gao. 2021. Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants. International Journal of Forecasting 37 (1):28–43. doi:10.1016/j.ijforecast.2020.02.008.
   Web of Science ®Google Scholar
• Chen, Z., C. Li, and W. Sun. 2020. Bitcoin price prediction using machine learning: An approach to sample dimension engineering. Journal of Computational and Applied Mathematics 365:112395. doi:10.1016/j.cam.2019.112395.
   Web of Science ®Google Scholar
• Cocco, L., R. Tonelli, and M. Marchesi. 2019. An agent-based artificial market model for studying the Bitcoin trading. IEEE Access 7:42908–20. doi:10.1109/ACCESS.2019.2907880.
   Web of Science ®Google Scholar
• EMA. 2020. Exponential (EMA). https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/exponential-ema/ (accessed June 5, 2020).
   Google Scholar
• Gaddala, K., and P. S. Raju. 2019. Enhanced self adaptive bat algorithm for optimal location of unified power quality conditioner. Journal of Computational Mechanics, Power System and Control 2 (3):28–38.
   Google Scholar
• Giudici, P., and I. Abu-Hashish. 2019. What determines Bitcoin exchange prices? A network VAR approach. Finance Research Letters 28:309–18. doi:10.1016/j.frl.2018.05.013.
   Web of Science ®Google Scholar
• Gyamerah, S. A. 2020. On forecasting the intraday Bitcoin price using ensemble of variational mode decomposition and generalized additive model. Journal of King Saud University – Computer and Information Sciences 34 (3):1003–9.
   Web of Science ®Google Scholar
• Haffar, A., and E. Le Fur. 2021. Structural vector error correction modelling of Bitcoin price. The Quarterly Review of Economics and Finance 80:170–78. doi:10.1016/j.qref.2021.02.010.
   Web of Science ®Google Scholar
• Huang, J. Z., W. Huang, and J. Ni. 2019. Predicting Bitcoin returns using high-dimensional technical indicators. The Journal of Finance and Data Science 5 (3):140–55. doi:10.1016/j.jfds.2018.10.001.
   Google Scholar
• Hui, C. H., C. F. Lo, P. H. Chau, and A. Wong. 2020. Does Bitcoin behave as a currency? A standard monetary model approach. International Review of Financial Analysis 70:101518. doi:10.1016/j.irfa.2020.101518.
   Web of Science ®Google Scholar
• Jang, H., and J. Lee. 2018. An empirical study on modeling and prediction of Bitcoin prices with Bayesian neural networks based on Blockchain information. IEEE Access 6:5427–37. doi:10.1109/ACCESS.2017.2779181.
   Web of Science ®Google Scholar
• Jay, P., V. Kalariya, P. Parmar, S. Tanwar, N. Kumar, and M. Alazab. 2020. Stochastic neural networks for cryptocurrency price prediction. IEEE Access 8:82804–18. doi:10.1109/ACCESS.2020.2990659.
   Web of Science ®Google Scholar
• Karasu, S., A. Altan, Z. Saraç, and R. Hacioğlu. 2018. Prediction of Bitcoin prices with machine learning methods using time series data. Paper presented at Signal Processing and Communications Applications Conference (SIU), Izmir, 1–4.
   Google Scholar
• Kurbucz, M. T. 2019. Predicting the price of Bitcoin by the most frequent edges of its transaction network. Economics Letters 184:108655. doi:10.1016/j.econlet.2019.108655.
   Web of Science ®Google Scholar
• Lahmiri, S., and S. Bekiros. 2020. Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market. Chaos, Solitons & Fractals 133:109641. doi:10.1016/j.chaos.2020.109641.
   Web of Science ®Google Scholar
• Li, Y., and W. Dai. 2020. Bitcoin price forecasting method based on CNN-LSTM hybrid neural network model. The Journal of Engineering 2020 (13):344–47. doi:10.1049/joe.2019.1203.
   Google Scholar
• Liu, L., S. Luo, F. Guo, and S. Tan. 2020. Multi-point shortest path planning based on an improved discrete bat algorithm. Applied Soft Computing 95:106498. 10.1016/j.asoc.2020.106498
   Web of Science ®Google Scholar
• Mallqui, D. C. A., and R. A. S. Fernandes. 2019. Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques. Applied Soft Computing 75:596–606. 10.1016/j.asoc.2018.11.038
   Web of Science ®Google Scholar
• Othman, A. H. A., S. Kassim, R. B. Rosman, and N. H. B. Redzuan. 2020a. Correction to: Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach. Journal of Revenue and Pricing Management 19 (5):331 doi:10.1057/s41272-020-00240-8.
   Web of Science ®Google Scholar
• Othman, A. H. A., S. Kassim, R. B. Rosman, and N. H. B. Redzuan. 2020b. Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach. Journal of Revenue and Pricing Management 19 (5):314–30. doi:10.1057/s41272-020-00229-3.
   Web of Science ®Google Scholar
• Preetha, N., and S. Praveena. 2018. Multiple feature sets and SVM classifier for the detection of diabetic retinopathy using retinal images. Multimedia Research 1 (1):17–26.
   Google Scholar
• Radityo, A., Q. Munajat, and I. Budi. 2017. Prediction of Bitcoin exchange rate to American dollar using artificial neural network methods. Paper presented at International Conference on Advanced Computer Science and Information Systems (ICACSIS), 433–38.
   Google Scholar
• Rajakumar, B. R. 2013. Impact of static and adaptive mutation techniques on genetic algorithm. International Journal of Hybrid Intelligent Systems 10 (1):11–22. doi:10.3233/HIS-120161.
   Google Scholar
• Rajakumar, B. R., and A. George. 2012. A new adaptive mutation technique for genetic algorithm. Paper presented at Proceedings of IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 1–7, Coimbatore, India.
   Google Scholar
• Rajakumar, B. R., S. M. Swamy, B. R. Rajakumar, and I. R. Valarmathi. 2013. Design of hybrid wind and photovoltaic power system using opposition-based genetic algorithm with Cauchy mutation. Paper presented at IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013), Chennai, India.
   Google Scholar
• Reddy, M. P. K., and M. R. Babu. 2019. Cluster head selection in IoT using enhanced self adaptive bat algorithm. Journal of Networking and Communication Systems 2 (4):23–32.
   Google Scholar
• ROC. 2020. Rate of change (ROC). https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/rate-of-change-roc/ (accessed June 5, 2020).
   Google Scholar
• RSI. 2020. Relative strength indicator (RSI). https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/relative-strength-indicator-rsi/ (accessed June 5, 2020).
   Google Scholar
• Saad, M., J. Choi, D. Nyang, J. Kim, and A. Mohaisen. 2020. Toward characterizing blockchain-based cryptocurrencies for highly accurate predictions. IEEE Systems Journal 14 (1):321–32. doi:10.1109/JSYST.2019.2927707.
   Web of Science ®Google Scholar
• Salisu, A. A., K. Isah, and L. O. Akanni. 2019. Improving the predictability of stock returns with Bitcoin prices. The North American Journal of Economics and Finance 48:857–67. doi:10.1016/j.najef.2018.08.010.
   Web of Science ®Google Scholar
• Swamy, S. M., B. R. Rajakumar, and I. R. Valarmathi. 2013. Design of hybrid wind and photovoltaic power system using opposition-based genetic algorithm with Cauchy mutation. Paper presented at IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013), Chennai, India. doi:10.1049/ic.2013.0361.
   Google Scholar
• Toyoda, K., P. T. Mathiopoulos, and T. Ohtsuki. 2019. A novel methodology for HYIP operators’ Bitcoin addresses identification. IEEE Access 7:74835–48. doi:10.1109/ACCESS.2019.2921087.
   Web of Science ®Google Scholar
• Valencia, F., A. Gómez-Espinosa, and B. Valdés-Aguirre. 2019. Price movement prediction of cryptocurrencies using sentiment analysis and machine learning. Entropy 21 (6):589. 10.3390/e21060589
   PubMed Web of Science ®Google Scholar
• Wang, G., and K. Hausken. 2022. A Bitcoin price prediction model assuming oscillatory growth and lengthening cycles. Cogent Economics & Finance 10 (1):2087287. doi:10.1080/23322039.2022.2087287.
   Web of Science ®Google Scholar
• Wang, Y., P. Wang, J. Zhang, Z. Cui, X. Cai, W. Zhang, and J. Chen. 2019. A novel bat algorithm with multiple strategies coupling for numerical optimization. Mathematics 7 (2):135. doi:10.3390/math7020135.
   Web of Science ®Google Scholar
• Yan, G., S. Wang, Z. Yang, and Y. Zhou. 2020. Dynamic game model for ranking Bitcoin transactions under GSP mechanism. IEEE Access 8:109198–206. doi:10.1109/ACCESS.2020.3001157.
   Web of Science ®Google Scholar