The study examines the problem of modeling and forecasting the price dynamics of crypto currencies. We use machine learning techniques to forecast the price of crypto currencies. The FB Prophet time series model and the LSTM recurrent neural network were selected to implement the study. Using the example of data from Binance (the most popular exchange in Ukraine) for the period from 06.07.2020 to 01.04.2023, prices for Bitcoin, Ethereum, Ripple, and Dogecoin were modeled and forecasted. The recurrent neural network of long-term memory showed significantly better results in forecasting according to the RMSE, MAE, and MAPE criteria, compared to the Naïve model, the traditional ARIMA model, and the FB Prophet results.
Aljadani, A. (2022). DLCP2F: a DL-based cryptocurrency price prediction framework. Discover Artificial Intelligence, 2(20). https://doi.org/10.1007/s44163-022-00036-2
Al-Nefaie, A. H., Aldhyani, T. H. H. (2022). Bitcoin price forecasting and trading: data analytics approaches. Electronics, 11(24), 4088, 2–18. https://doi.org/10.3390/electronics11244088
Amase, W. (2023). Countries where cryptocurrency is legal vs illegal. CoinGecko, 11 December 2023. Retrieved from https://www.coingecko.com/research/publications/crypto-legal-countries
Ammer, M. A., Aldhyani, T. H. H. (2022). Deep learning algorithms to predict cryptocurrency fluctuation prices: increasing investment awareness. Electronics, 11(15), 2349. https://doi.org/10.3390/electronics11152349
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control, 5th ed. New Jersey: Wiley.
Cheng, J., Tiwari, S., Khaled, D., Mahendru, M., Shahzad, U. (2024) Forecasting Bitcoin prices using artificial intelligence: combination of ML, SARIMA, and Facebook Prophet models. Technological Forecasting and Social Change, 198, 122938. https://doi.org/10.1016/j.techfore.2023.122938
Corbet, S., Lucey, B., Urquhart, A., Yarovays, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182–199. https://doi.org/10.1016/j.irfa.2018.09.003
Couts, D., Grether, D., Nerlove, M. (1966). Forecasting non-stationary economic time series. Management Science, 13(1), 1–21. http://www.jstor.org/stable/2627909
Derbentsev, V., Datsenko, N., Stepanenko, O., Bezkorovainyi, V. (2019). Forecasting cryptocurrency prices time series using machine learning approach. SHS Web of Conferences, 65, 02001. https://doi.org/10.1051/shsconf/20196502001
Feng, J., He, X., Teng, Q., Ren, C., Chen, H., Li, Y. (2019). Reconstruction of porous media from extremely limited information using conditional generative adversarial networks. Physical Review E, 100, 033308. https://doi.org/10.1103/PhysRevE.100.033308
Persson, E. (2022). Forecasting Efficiency in Cryptocurrency. Markets A Machine Learning Case Study. Retrieved from https://www.diva-portal.org/smash/get/diva2:1711510/FULLTEXT01.pdf
Gagnidze, A., Iavich, M. (2021). Time-series forecasting of Bitcoin prices. East European University Collection of Scientific Papers, 2(2020), 46–53.
Garlapati, A., Krishna, D. R., Garlapati, K., m. Srikara Yaswanth, N., Rahul, U., Narayanan, G. (2021). Stock price prediction using Facebook Prophet and Arima models. 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India, 2021. https://doi.org/10.1109/I2CT51068.2021.9418057
Lindemann, B., Maschler, B., Sahlab, N., Weyrich, M (2021). A survey on anomaly detection for technical systems using LSTM networks. Computers in Industry, 131, 103498. https://doi.org/10.1016/j.compind.2021.103498
Livieris, I. E., Pintelas, E., Stavroyiannis, S., Pintelas, P. (2020). Ensemble deep learning models for forecasting cryptocurrency time-series. Algorithms, 13, 121. https://doi.org/10.3390/a13050121
Luo, C., Pan, L., Chen, B., Xu, H. (2022) Bitcoin price forecasting: an integrated approach using hybrid LSTM-ELS models. Mathematical Problems in Engineering, 2022, 2126518. https://doi.org/10.1155/2022/2126518
Malinovska, A. (2023). Crypto currency tax: percentage, who should pay and features (In Ukrainian). Fakty, 1 December 2023. Retrieved from https://fakty.com.ua/ua/ukraine/ekonomika/20231201-podatok-na-kryptovalyutu-vidsotok-hto-maye-splachuvaty-ta-osoblyvosti/
Pilipchenko, A., Kuzminsky, V., Chumachenko, O. (2021). Using methods of technical analysis to forecast the cryptocurrency market. Včenì zapiski unìversitetu "KROK", 4(64), 28–35. https://doi.org/10.31732/2663-2209-2021-64-28-35
Plevris, V., Solorzano, G., Bakas, N., Ben Seghier, M. (2022). Investigation of performance metrics in regression analysis and machine learning-based prediction models. ECCOMAS Congress 2022, 5, 9 June 2022. Oslo. https://doi.org/10.23967/eccomas.2022.155
Pronchakov, Y., Bugaienko, O. (2019). Methods of forecasting the prices of cryptocurrency on the financial markets. Technology Transfer: Innovative Solutions In Social Sciences and Humanities, 13–16. https://doi.org/10.21303/2613-5647.2019.00927
Sherstinsky, A. (2020). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306. https://doi.org/10.1016/j.physd.2019.132306
Sivaramakrishnan, S., Fernandez, T. R., Babukarthikm R. G., Premalatha, S. (2021). 4 forecasting time series data using ARIMA and Facebook Prophet models. In Big Data Management in Sensing: Applications in AI and IoT, 47–60. River Publishers. Retrieved from http://ieeexplore.ieee.org/document/9606779
Taylor, S. J., Letham, B. (2017). Forecasting at scale. PeerJ Preprints, 5, e3190v2. https://doi.org/10.7287/peerj.preprints.3190v2
Vasselin, H., Bertrand, J. (2021). Is Facebook Prophet suited for doing good predictions in a real-world project? Artefact. Retrieved from https://www.artefact.com/blog/is-facebook-prophet-suited-for-doing-good-predictions-in-a-real-world-project/
Verkhovna Rada of Ukraine (2023). The Draft Law of Ukraine on Amendments to the Tax Code of Ukraine and Other Legislative Acts of Ukraine Concerning the Regulation of Virtual Asset Turnover in Ukraine No. 10225-1 dated 17.11.2023 (In Ukrainian). Retrieved from https://itd.rada.gov.ua/billInfo/Bills/Card/43232
Verkhovna Rada of Ukraine (2024). Law of Ukraine "On Virtual Assets" No 2074-IX Edition of 01.01.2024 (did not enter into force) (In Ukrainian). Retrieved from https://zakon.rada.gov.ua/laws/show/2074-20
Whittle, P. (1953). Estimation and information in stationary time series. Arkiv för Matematik, 2, 423–434. https://doi.org/10.1007/BF02590998
