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dc.contributor.authorDerbentsev, Vasily-
dc.contributor.authorMatviychuk, Andriy-
dc.contributor.authorСоловйов, Володимир Миколайович-
dc.date.accessioned2020-12-25T17:12:11Z-
dc.date.available2020-12-25T17:12:11Z-
dc.date.issued2020-07-30-
dc.identifier.citationDerbentsev V. Forecasting of Cryptocurrency Prices Using Machine Learning / Vasily Derbentsev, Andriy Matviychuk, Vladimir N. Soloviev // Advanced Studies of Financial Technologies and Cryptocurrency Markets / Editors : Lukáš Pichl, Cheoljun Eom, Enrico Scalas, Taisei Kaizoji. - Singapore : Springer, 2020. - DOI : 10.1007/978-981-15-4498-9_12uk_UA
dc.identifier.isbn978-981-15-4497-2-
dc.identifier.urihttp://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4137-
dc.identifier.urihttps://doi.org/10.1007/978-981-15-4498-9_12-
dc.descriptionAkyildirim, E., Goncuy, A., & Sensoy, A. (2018). Prediction of Cryptocurrency Returns Using Machine Learning. https://www.researchgate.net/publication/329322600. Accessed November 15, 2019. Albuquerque, Y., de Sá, J., Padula, A., & Montenegro, M. (2018). The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regression. Expert Systems with Applications, 97, 177–192. https://doi.org/10.1016/j.eswa.2017. 12.004. Alessandretti, L., ElBahrawy, A., Aiello, L., & Baronchelli, A. (2018). Anticipating cryptocurrency prices using machine learning. Hindawi Complexity. https://doi.org/10.1155/2018/8983590. Amjad, M., & Shah, D. (2016). Trading Bitcoin and online time series prediction. NIPS 2016 Time Series Workshop. http://proceedings.mlr.press/v55/amjad16.pdf. Accessed November 15, 2019. Belinskyi, A., Soloviev, V., Semerikov, S., & Solovieva, V. (2019). Detecting stock crashes using Levy distribution. In CEUR Workshop Proceedings (Vol. 2422, pp. 420–433). http://ceur-ws.org/ Vol-2422/paper_34.pdf. Bontempi, G., Taieb, S., & Borgne, Y. (2013). Machine learning strategies for time series forecasting. In European Business Intelligence Summer School eBISS 2012 (pp. 62–77). Berlin, Heidelberg: Springer. Boyacioglu, M., & Baykan, O. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock. Exchange Expert Systems with Applications, 38(5), 5311–5319. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. Breiman, L., Friedman, H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Belmont, NJ: Wadsworth International Group. Catania, L., & Grassi, S. (2017). Modelling crypto-currencies financial time-series. CEIS Research Paper (15(8), pp. 1–39). https://ideas.repec.org/p/rtv/ceisrp/417.html. Accessed November 15, 2019. Cheah, E. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of bitcoin. Economic Letters, 130, 32–36. Ciaian, P. (2016). The economics of BitCoin price formation. Applied Economics, 48(19), 1799– 1815. CNBC. (2018). Top Economists Stiglitz, Roubini and Rogoff Renew Bitcoin Doom Scenarios. https://www.cnbc.com/2018/07/09/nobel-prize-winning-economist-joseph-stiglitz-criticizesbitcoin.html. Accessed November 15, 2019. Derbentsev, V., Datsenko, N., Stepanenko, O., & Bezkorovainyi, V. (2019a). Forecasting cryptocurrency prices time series using machine learning. In CEUR Workshop Proceedings (Vol. 2422, pp. 320–334). Derbentsev, V., Kibalnyk, L., & Radzihovska, Y. (2019b). Modelling multifractal properties of cryptocurrency market using Hurst exponent and detrended fluctuation analysis. PEN, 7(2), 690– 701. Flach, P. (2012). Machine learning: The art and science of algorithms that make sense of data. Cambridge, UK: Cambridge University Press. Hitam, N. A., & Ismail, A. R. (2018). Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting. https://www.researchgate.net/publication/327415267. Accessed November 15, 2019. Kennis, M. (2018). A Multi-channel Online Discourse as an Indicator for Bitcoin Price and Volume. arXiv:1811.03146v1 [q-fin.ST]. Accessed November 6, 2018. Krugman, P. (2013). Bits and Barbarism. http://www.nytimes.com/2013/12/23/opinion/ krugmanbits-and-barbarism.html. Accessed November 15, 2019. Kumar, M. (2006). Forecasting stock index movement: A comparison of support vector machines and random forest. SSRN Working Paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id= 876544. Accessed November 15, 2019. Matviychuk, A. (2006). Fuzzy logic approach to identification and forecasting of financial time series using Elliott wave theory. Fuzzy Economic Review, 11(2), 51–68. Matviychuk, A. V. (2011). Shtuchnyi intelekt v ekonomitsi: neironni merezhi, nechitka logika (Artificial Intelligence in Economics: Neural Networks, Fuzzy Logic), Kyiv, KNEU (in Ukrainian). McNally, S. (2016). Predicting the price of Bitcoin using machine learning (Doctoral dissertation). National College of Ireland, Dublin. Okasha, M. K. (2014). Using support vector machines in financial time series forecasting. Statistics, 4(1), 28–39. https://doi.org/10.5923/j.statistics.20140401.03. Peng, Y., Henrique, P., & Albuquerque, M. (2018). The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regression. Expert Systems with Applications, 97, 177–192. Persio, L., & Honchar, O. (2018). Multitask machine learning for financial forecasting.International Journal of Circuits, Systems and Signal Processing, 12, 444–451. Popper, N. (2015). Digital gold: Bitcoin and the inside story of the misfits and millionaires trying to reinvent money. New York, NY: Harper Collins Publisher. Rebane, J., & Karlsson, I. (2018). Seq2Seq RNNs and ARIMA models for cryptocurrency prediction: A comparative study. In SIGKDD Fintech’18. https://doi.org/10.475/123_4. Saxena, A., & Sukumar, T. (2018). Predicting bitcoin price using LSTM and compare its predictability with ARIMA model. International Journal of Pure Applied Mathematics, 119(17), 2591–2600. Selmi, R., Tiwari, A., & Hammoudeh, S. (2018). Efficiency or speculation? A dynamic analysis of the Bitcoin market. Economic Bulletin, 38(4), 2037–2046. Soloviev, V., & Belinskij, A. (2016). Methods of Nonlinear Dynamics and the Construction of Cryptocurrency Crisis Phenomena Precursors. arXiv:1807.05837; https://arxiv.org/abs/1807.05837. Accessed November 15, 2019. Soloviev, V., & Belinskij, A. (2019). Complex systems theory and crashes of cryptocurrency market. In Communications in Computer and Information Science (Vol. 1007, pp. 276–297). https://link. springer.com/chapter/10.1007/978-3-030-13929-2_14. Soloviev, V., Belinskij, A., & Solovieva, V. (2019a). Entropy analysis of crisis phenomena for DJIA index. In CEUR Workshop Proceedings (Vol. 2393, pp. 434–449). http://ceur-ws.org/Vol-2393/ paper_375.pdf. Soloviev, V., Serdiuk, O., Semerikov, S., & Kohut-Ferens, O. (2019b). Recurrence entropy and financial crashes. In Proceedings of the 7th International Conference on Modeling, Development and Strategic Management of Economic System, Ivano-Frankivsk, Ukraine, October 24–25, 2019. https://www.atlantis-press.com/proceedings/. Soloviev, V., Solovieva, V., Tuliakova, A., & Ivanova, M. (2019c). Construction of crisis precursors in multiplex networks. In Proceedings of the 7th International Conference on Modeling, Development and Strategic Management of Economic System, Ivano-Frankivsk, Ukraine, October 24–25, 2019. https://www.atlantis-press.com/proceedings/. Varghade, P., & Patel, R. (2012). Comparison of SVR and decision trees for financial series prediction. IJACTE, 1(1), 101–105. Vigna, P., & Casey, M. J. (2015). The age of cryptocurrency: How Bitcoin and digital money are challenging the global economic order. New York, NY: St. Martin’s Press. Wang, M., Zhao, L., Du, R., Wang, C., Chen, L., Tian, L., et al. (2018). A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms. Applied Energy, 220, 480–495. https://doi.org/10.1016/j.apenergy.2018.03.148. Yahoo Finance. (2019). https://finance.yahoo.com. Accessed November 15, 2019. Yao, Y., Yi, J., & Zhai, S. (2018). Predictive analysis of cryptocurrency price using deep learning. International Journal of Engineering & Technology, 7(3.27), 258–264.-
dc.description.abstractOur study is devoted to the problems of the short-term forecasting cryptocurrency time series using machine learning (ML) approach. Focus on studying of the financial time series allows to analyze the methodological principles, including the advantages and disadvantages of using ML algorithms. The 90-day time horizon of the dynamics of the three most capitalized cryptocurrencies (Bitcoin, Ethereum, Ripple) was estimated using the Binary Autoregressive Tree model (BART), Neural Networks (multilayer perceptron, MLP) and an ensemble of Classification and Regression Trees models—Random Forest (RF). The advantange of the developed models is that their application does not impose rigid restrictions on the statistical properties of the studied cryptocurrencies time series, with only the past values of the target variable being used as predictors. Comparative analysis of the predictive ability of the constructed models showed that all the models adequately describe the dynamics of the cryptocurrencies with the mean absolute persentage error (MAPE) for the BART andMLPmodels averaging 3.5%, and for RFmodels within 5%. Since for trading perspective it is of interest to predict the direction of a change in price or trend, rather than its numerical value, the practical application of BART model was also demonstrated in the forecasting of the direction of change in price for a 90-day period. To this end, a model of binary classification was used in the methodology for assessing the degree of attractiveness of cryptocurrencies as an innovative financial instrument. Conducted computer simulations have confirmed the feasibility of using the machine learning methods and models for the short-term forecasting of financial time series. Constructed models and their ensembles can be the basis for the algorithms for automated trading systems for Internet trading.uk_UA
dc.language.isoenuk_UA
dc.publisherSpringeruk_UA
dc.subjectbinary autoregressive tree modeluk_UA
dc.subjectcryptocurrency pricesuk_UA
dc.subjectfinancial time seriesuk_UA
dc.subjectmachine learninguk_UA
dc.subjectneural networkuk_UA
dc.subjectregression and classification tree ensembleuk_UA
dc.subjectshort-term forecastinguk_UA
dc.titleForecasting of Cryptocurrency Prices Using Machine Learninguk_UA
dc.typeBook chapteruk_UA
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