Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

2147-3129 2147-3188

Bitlis Eren University

10.17798/bitlisfen.1479725

Artificial Intelligence (Other)

Yapay Zeka (Diğer)

Hybrid Optimal Time Series Modeling for Cryptocurrency Price Prediction: Feature Selection, Structure and Hyperparameter Optimization

https://orcid.org/0000-0003-4165-0512

Bülbül

Mehmet Akif

Kayseri University

09 26 2024

13 3 731 743 05 07 2024 07 09 2024

2012

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

The prime aim of the research is to forecast the future value of bitcoin that is commonly known as pioneer of the Cryptocurrency market by constructing hybrid structure over the time series. In this perspective, two separate hybrid structures were created by using Artificial Neural Network (ANN) together with Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO). By using the hybrid structures created, both the network model and the hyper parameters in the network structure, together with the time intervals of the daily closing prices and how many data should be taken retrospectively, were optimized. Employing the created GA-ANN (DCP1) and PSO-ANN (DCP2) hybrid structures and the 721-day Bitcoin series, the goal of accurately predicting the values that Bitcoin will receive has been achieved. According to the comparative results obtained in line with the stated objectives and targets, it has been determined that the structure obtained with the DCP1 hybrid model has a success rate of 99% and 97.54% in training and validation, respectively. It should also, be underlined that the DCP1 model showed 47% better results than the DCP2 hybrid model. With the proposed hybrid structure, the network parameters and network model that should be used in the ANN network structure are optimized in order to obtain more efficient results in cryptocurrency price forecasting, while optimizing which input data should be used in terms of frequency and closing price to be chosen.

Genetic Algorithm Particial Swarm Optimization Algorithm Artificial Neural Network Model and Hyperparameter Optimization Cryptocurrency Price Forecasting

[1] K. Meng and K. Khan, “Is cryptocurrency Efficient? A High-Frequency Asymmetric Multifractality Analysis,” Comput. Econ., pp. 1-22, 2023, doi: 10.1007/s10614-023-10402-6.

[2] D. Shang, Z. Yan, L. Zhang, and Z. Cui, “Digital financial asset price fluctuation forecasting in digital economy era using blockchain information: A reconstructed dynamic-bound Levenberg–Marquardt neural-network approach,” Expert Syst. Appl., vol. 228, 2023, doi: 10.1016/j.eswa.2023.120329.

[3] M. Campbell-Verduyn, “Bitcoin, crypto-coins, and global anti-money laundering governance,” Crime, Law Soc. Chang., vol. 69, pp. 283-305, 2018, doi: 10.1007/s10611-017-9756-5.

[4] J. Bhosale and S. Mavale, “Volatility of select Crypto-currencies: A comparison of Bitcoin, Ethereum and Litecoin,” Annu. Res. J. SCMS, Pune, vol.6, no.1, pp. 132-141, 2018.

[5] U. Rahardja, I. Handayani, and A. A. Ningrum, “Pemanfaatan Sistem iMe Berbasis WordPress sebagai Official Site RCEP pada Perguruan Tinggi,” Creat. Inf. Technol. J., vol. 4, no. 3, pp. 207, 2018, doi: 10.24076/citec.2017v4i3.111.

[6] B. Tripathi and R. K. Sharma, “Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks,” Comput. Econ., vol. 62, no. 4, pp. 1919-1945, 2023, doi: 10.1007/s10614-022-10325-8.

[7] E. Işık, N. Ademović, E. Harirchian, F. Avcil, A. Büyüksaraç, M. Hadzima-Nyarko, … and B. Antep, “Determination of Natural Fundamental Period of Minarets by Using Artificial Neural Network and Assess the Impact of Different Materials on Their Seismic Vulnerability,” Appl. Sci., vol. 13, no. 2, pp. 809, 2023, doi: 10.3390/app13020809.

[8] I. Ayus, N. Natarajan, and D. Gupta, “Prediction of Water Level Using Machine Learning and Deep Learning Techniques,” Iran. J. Sci. Technol. - Trans. Civ. Eng., vol. 47, no. 4, pp. 2437–2447, 2023, doi: 10.1007/s40996-023-01053-6.

[9] J. V. Tembhurne, N. Hebbar, H. Y. Patil, and T. Diwan, “Skin cancer detection using ensemble of machine learning and deep learning techniques,” Multimed. Tools Appl., vol. 82, no. 18, pp. 27501-27524, 2023, doi: 10.1007/s11042-023-14697-3.

[10] S. Goutte, H. V. Le, F. Liu, and H. J. von Mettenheim, “Deep learning and technical analysis in cryptocurrency market,” Financ. Res. Lett., vol. 54, pp. 103809, 2023, doi: 10.1016/j.frl.2023.103809.

[11] Z. Zhou, Z. Song, H. Xiao, and T. Ren, “Multi-source data driven cryptocurrency price movement prediction and portfolio optimization,” Expert Syst. Appl., vol. 219, pp. 119600, 2023, doi: 10.1016/j.eswa.2023.119600.

[12] N. A. Rashid and M. T. Ismail, “Modelling and Forecasting the Trend in Cryptocurrency Prices,” J. Inf. Commun. Technol., vol. 22, no. 3, pp. 449–501, 2023, doi: 10.32890/jict2023.22.3.6.

[13] L. T. Mariappan, J. A. Pandian, V. D. Kumar, O. Geman, I. Chiuchisan, and C. Năstase, “A Forecasting Approach to Cryptocurrency Price Index Using Reinforcement Learning,” Appl. Sci., vol. 13, no.4, pp.2692, 2023, doi: 10.3390/app13042692.

[14] K. Murray, A. Rossi, D. Carraro, and A. Visentin, “On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles,” Forecasting, vol. 5, no. 1, pp. 196–209, 2023, doi: 10.3390/forecast5010010.

[15] S. C. Nayak, S. Das, S. Dehuri, and S. B. Cho, “An Elitist Artificial Electric Field Algorithm Based Random Vector Functional Link Network for Cryptocurrency Prices Forecasting” IEEE Access, 2023, doi: 10.1109/ACCESS.2023.3283571.

[16] L. Al Hawi, S. Sharqawi, Q. A. Al-Haija, and A. Qusef, “Empirical Evaluation of Machine Learning Performance in Forecasting Cryptocurrencies,” J. Adv. Inf. Technol., vol. 14, no. 4, pp. 639–647, 2023, doi: 10.12720/jait.14.4.639-647.

[17] K. Ateeq, A. A. Al Zarooni, A. Rehman, and M. A. Khan, “A Mechanism for Bitcoin Price Forecasting using Deep Learning,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 8, 2023, doi: 10.14569/IJACSA.2023.0140849.

[18] I. Alarab and S. Prakoonwit, “Graph-Based LSTM for Anti-money Laundering: Experimenting Temporal Graph Convolutional Network with Bitcoin Data,” Neural Process. Lett., vol. 55, no. 1, pp. 689-707, 2023, doi: 10.1007/s11063-022-10904-8.

[19] R. M. Aziz, M. F. Baluch, S. Patel, and P. Kumar, “A Machine Learning Based Approach to Detect the Ethereum Fraud Transactions with Limited Attributes,” Karbala Int. J. Mod. Sci., vol. 8, no. 2, pp. 139-151, 2022, doi: 10.33640/2405-609X.3229.

[20] J. Li, "The comparison of lstm, lgbm, and cnn in stock volatility prediction," In: 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022), pp. 905–909 2022. Atlantis Press. doi: 10.2991/aebmr.k.220307.147.

[21] S. Kazeminia, H. Sajedi, and M. Arjmand, "Real-time bitcoin price prediction using hybrid 2d-cnn lstm model," In: 2023 9th International Conference on Web Research (ICWR), pp. 173–178, 2023. IEEE. doi: 10.1109/ICWR57742.2023.10139275.

[22] M. A. Bülbül and C. Öztürk, “Optimization, Modeling and Implementation of Plant Water Consumption Control Using Genetic Algorithm and Artificial Neural Network in a Hybrid Structure,” Arab. J. Sci. Eng., vol. 47, no. 2, pp. 2329-2343, 2022, doi: 10.1007/s13369-021-06168-4.

[23] P. Matić, O. Bego, and M. Maleš, “Complex Hydrological System Inflow Prediction using Artificial Neural Network,” Teh. Vjesn., vol. 29, no. 1, pp. 172-177, 2022, doi: 10.17559/TV-20200721133924.

[24] D. Nahavandi, R. Alizadehsani, A. Khosravi, and U. R. Acharya, “Application of artificial intelligence in wearable devices: Opportunities and challenges,” Computer Methods and Programs in Biomedicine. vol. 213, pp. 106541, 2022. doi: 10.1016/j.cmpb.2021.106541.

[25] S. S. Ul Hasan, A. Ghani, I. U. Din, A. Almogren, and A. Altameem, “IoT devices authentication using artificial neural network,” Comput. Mater. Contin., vol. 70, pp. 3701-3716, 2022, doi: 10.32604/cmc.2022.020624.

[26] D. P. Soman, P. Kalaichelvi, and T. K. Radhakrishnan, “Density modelling and apparent molar volume of ionic liquid 1-butyl-3-methylimidazolium bromide in water,” J. Brazilian Soc. Mech. Sci. Eng., vol. 44, no. 3, pp. 1–14, 2022, doi: 10.1007/s40430-021-03248-2.

[27] M. A. Bülbül, “Optimization of artificial neural network structure and hyperparameters in hybrid model by genetic algorithm: iOS–android application for breast cancer diagnosis/prediction,” J. Supercomput., vol. 80, no. 4, pp. 4533-4553, 2023, doi: 10.1007/s11227-023-05635-z.

[28] B. Sun and Y. Zhou, “Bayesian network structure learning with improved genetic algorithm,” Int. J. Intell. Syst., vol. 37, no. 9, pp. 6023-6047, 2022, doi: 10.1002/int.22833.

[29] A. Taha and O. Barukab, “Android Malware Classification Using Optimized Ensemble Learning Based on Genetic Algorithms,” Sustain., vol. 14, no. 21, pp. 14406, 2022, doi: 10.3390/su142114406.

[30] X. Liu, D. Jiang, B. Tao, G. Jiang, Y. Sun, J. Kong, X. Tong, G. Zhao and B. Chen, “Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots,” Front. Bioeng. Biotechnol., vol. 9, pp. 7937822, 022, doi: 10.3389/fbioe.2021.793782.

[31] M. A. Bülbül, C. Öztürk, and M. F. Işık, “Optimization of Climatic Conditions Affecting Determination of the Amount of Water Needed by Plants in Relation to Their Life Cycle with Particle Swarm Optimization, and Determining the Optimum Irrigation Schedule,” Comput. J., vol. 65, no. 10, pp. 2654-2663, 2022, doi: 10.1093/comjnl/bxab097.

[32] R. P. de Gusmão and F. de A. T. de Carvalho, “Clustering of multi-view relational data based on particle swarm optimization,” Expert Syst. Appl., vol. 123, pp. 34-53, 2019, doi: 10.1016/j.eswa.2018.12.053.

[33] S. Papadakis and M. Markaki, “An in depth economic restructuring framework by using particle swarm optimization,” J. Clean. Prod., vol. 215, pp. 329–342, 2019, doi: 10.1016/j.jclepro.2019.01.041.

[34] J. Zhu, J. Liu, Y. Chen, X. Xue, and S. Sun, “Binary Restructuring Particle Swarm Optimization and Its Application,” Biomimetics, vol. 8, no. 2, pp. 266, 2023, doi: 10.3390/biomimetics8020266.

[35] S. Xu , X. Yang, S. Zhang, X. Zheng, F. Zheng, Y. Liu, H. Zhang, Q. Ye and L. Li, “Machine learning models for orthokeratology lens fitting and axial length prediction,” Ophthalmic Physiol. Opt., vol. 43, no. 6, pp. 1462-1468, 2023, doi: 10.1111/opo.13212.

[36] M. A. Bülbül, E. Harirchian, M. F. Işık, S. E. Aghakouchaki Hosseini, and E. Işık, “A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings,” Appl. Sci., vol. 12, no. 10, 2022, doi: 10.3390/app12105138.

[37] M. F. Işık, F. Avcil, E. Harirchian, M. A. Bülbül, M. Hadzima-Nyarko, E. Işık, R. İzol and D. Radu, “A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings,” Sustainability, vol. 15, no. 12, pp. 9715, 2023, doi: 10.3390/su15129715.

[38] coinmarketcap.com: Bitcoin price today. https://coinmarketcap.com/currencies/ bitcoin/historical-data/. [Date of access: September 2022]

[39] N. M. Razali, J. Geraghty, "Genetic algorithm performance with different selection strategies in solving tsp," In: Proceedings of the World Congress on Engineering, vol. 2, pp. 1–6, 2011. International Association of Engineers Hong Kong, China.

[40] I. Jannoud, Y. Jaradat, M. Z. Masoud, A. Manasrah, and M. Alia, “The role of genetic algorithm selection operators in extending wsn stability period: A comparative study,” Electron., vol. 11, no. 1, pp.28, 2022, doi: 10.3390/electronics11010028.