Abstract
Machine learning as a subset of artificial intelligence presents a promising set of algorithms with an ability to gather experience and learn from provided data. This coupled with the expanding availability of computational resources and information transparency has made it possible to utilize algorithms to forecast prices. In recent years, cryptocurrency has increased in popularity and has seen wider adoption as a payment method. However, due to the volatile nature of the cryptocurrency market, casting accurate predictions can be quite challenging. One promising approach is the application of long-short-term memory artificial neural networks to time-series price data to attain results. The forecasting accuracy of machine learning models is highly dependent on adequate hyperparameter settings. Thus, this work, an improved version of the arithmetic optimization algorithm, is tasked with selecting optimal values of a long-short term network casting price predictions. The proposed approach has been tested on publicly available real-world Ethereum trading price data and according to the results of comparative analysis with other contemporary metaheuristics, it has been concluded that the proposed method achieved excellent results, and outperformed aforementioned algorithms in one and four-step ahead predictions.
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References
Mukhopadhyay, U., Skjellum, A., Hambolu, O., Oakley, J., Yu, L., Brooks, R.R.: A brief survey of cryptocurrency systems. In: 2016 14th Annual Conference on Privacy, Security and Trust (PST), pp. 745–752 (2016)
Dutta, A., Kumar, S., Basu, M.: A gated recurrent unit approach to bitcoin price prediction. arXiv preprint arXiv:1912.11166, December 2019
Parfenov, D.: Efficiency linkages between cryptocurrencies, equities and commodities at different time frames. Procedia Comput. Sci. 199, 182–189 (2022)
Philippas, D.: Media attention and bitcoin prices. SSRN Electron. J. (2019)
Buterin, V.: A next generation smart contract & decentralized application platform (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC 1997), pp. 303–308 (1997)
Karaboga, D.: Artificial bee colony algorithm. scholarpedia 5(3), 6915 (2010)
Yang, X.-S., Slowik, A.: Firefly algorithm. In: Swarm Intelligence Algorithms, pp. 163–174. CRC Press (2020)
Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)
Yang, X.-S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29, 464–483 (2012)
Wolpert, D.H, Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
Bukumira, M., Antonijevic, M., Jovanovic, D., Zivkovic, M., Mladenovic, D., Kunjadic, G.: Carrot grading system using computer vision feature parameters and a cascaded graph convolutional neural network. J. Electron. Imaging 31(6), 061815 (2022)
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), 2272 (2022)
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. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-09835-2_16
Zivkovic, M., Bacanin, N., Arandjelovic, J., Strumberger, I., Venkatachalam, K.: Firefly algorithm and deep neural network approach for intrusion detection. In: Unhelker, B., Pandey, H.M., Raj, G. (eds.) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol. 925. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-4831-2_1
Salb, M., Jovanovic, L., Zivkovic, M., Tuba, E., Elsadai, A., Bacanin, N.: Training logistic regression model by enhanced moth flame optimizer for spam email classification. In: Smys, S., Lafata, P., Palanisamy, R., Kamel, K.A. (eds.) Computer Networks and Inventive Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol. 141. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-3035-5_56
Bezdan, T., Zivkovic, M., Bacanin, N., Chhabra, A., Suresh, M.: Feature selection by hybrid brain storm optimization algorithm for covid-19 classification. J. Comput. Biol. 29(6), 515–529 (2022)
Zivkovic, M., et al.: Covid-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain. Cities Soc. 66, 102669 (2021)
Bezdan, T., Zivkovic, M., Tuba, E., Strumberger, I., Bacanin, N., Tuba, M.: Glioma brain tumor grade classification from MRI using convolutional neural networks designed by modified FA. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I.U., Cebi, S., Tolga, A.C. (eds.) INFUS 2020. AISC, vol. 1197, pp. 955–963. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51156-2_111
Abualigah, L., Diabat, A., Mirjalili, S., Elaziz, M.E.A., Gandomi, A.: The arithmetic optimization algorithm. Comput. Methods Appl. Mech. Eng. 376, 113609 (2021)
Chen, M., Zhou, Y., Luo, Q.: An improved arithmetic optimization algorithm for numerical optimization problems. Mathematics 10(12), 2152 (2022)
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Stankovic, M., Jovanovic, L., Bacanin, N., Zivkovic, M., Antonijevic, M., Bisevac, P. (2023). Tuned Long Short-Term Memory Model for Ethereum Price Forecasting Through an Arithmetic Optimization Algorithm. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-27499-2_31
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