Сравнение современных методов прогнозирования доходностей криптовалют с бенчмарком SARIMA
Аннотация
Статья посвящена сравнительному анализу современных методов прогнозирования доходностей криптовалют. Рассматриваются классические статистические модели (SARIMA), методы машинного обучения, нейросетевые архитектуры (LSTM, трансформеры) и метод группового учета аргументов (МГУА). Цель исследования – выявить сильные и слабые стороны различных подходов при краткосрочном и среднесрочном прогнозировании.
Эмпирический анализ проведен на временных рядах 13 крупнейших криптовалют за период от 3 до 10 лет. Данные были преобразованы в логарифмические доходности, использовались блочная кросс-валидация и автоматизированный подбор гиперпараметров. Рассматривались горизонты 1, 7 и 30 дней, а также рекурсивные и масштабируемые методы многошагового прогнозирования.
Результаты показали, что для краткосрочных горизонтов наиболее точными оказались модели МГУА, тогда как LSTM демонстрировала худшие показатели. На месячном горизонте лидерство перешло к трансформеру Chronos, применявшемуся в режиме обучения внутри контекста. SARIMA сохранила устойчивость на средних горизонтах и подтвердила роль надежного бенчмарка. Масштабируемые методы позволили снизить ошибки по сравнению с рекурсивным прогнозом.
Полученные выводы подчеркивают отсутствие универсального алгоритма и важность выбора метода в зависимости от горизонта и задач анализа.
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Литература
Aggarwal A., Gupta I., Garg N., Goel A. (2019) Deep Learning Approach to Determine the Impact of Socio Economic Factors on Bitcoin Price Prediction. 12th International Conference on Contemporary Computing (IC3), pp. 1–5. DOI: https://doi.org/10.1109/IC3.2019.8844928
Albariqi R., Winarko E. (2020) Prediction of Bitcoin Price Change using Neural Networks. International Conference on Smart Technology and Applications (ICoSTA), pp. 1–4. DOI: https://doi.org/10.1109/ICoSTA48221.2020.1570610936
Ansari A.F., Stella L., Turkmen C., Zhang X., Mercado P., Shen H., Shchur O., Rangapuram S.S., Arango S.P., Kapoor S., Zschiegner J., Maddix D.C., Wang H., Mahoney M.W., Torkkola K., Wilson A.G., Bohlke-Schneider M., Wang Y. (2024) Chronos: Learning the Language of Time Series. Transactions on Machine Learning Research.
Bayburin B., Mogilev P., Alexandrov M., Cardiff J., Koshulko O. (2020) Joint Mid-Term Forecast of Cryptocurrencies in Technique of Inductive Modelling (on Example of XRP, Waves, ETH). 31st Conference of FRUCT Association.
Box G.E.P., Jenkins G.M., Reinsel G.C., Ljung G.M. (2015) Time Series Analysis: Forecasting and Control. 5th ed. John Wiley and Sons Inc. Vol. 5.
Chen M., Narwal N., Schultz M. (2019) Predicting Price Changes in Ethereum. International Journal of Computation Science and Engineering.
Chen T., Guestrin C. (2016) XGBoost: A Scalable Tree Boosting System. The 22nd ACM SIGKDD International Conference, pp. 785–794. DOI: https://doi.org/10.1145/2939672.2939785
Herremans D., Low K. (2022) Forecasting Bitcoin Volatility Spikes from Whale Transactions and CryptoQuant Data Using Synthesizer Transformer Models [Report]. DOI: https://doi.org/10.2139/ssrn.4247684
Hochreiter S., Schmidhuber J. (1997) Long Short-Term Memory. Neural Computation, 9, 8, pp. 1735–1780. DOI: https://doi.org/10.1162/neco.1997.9.8.1735
Hou A.J., Wang W., Chen C.Y., Härdle W.K. (2020) Pricing Cryptocurrency Options. Journal of Financial Econometrics, 18, 2, pp. 250–279.
Hyndman R.J., Khandakar Y. (2008) Automatic Time Series Forecasting: The Forecast Package for R. Journal of Statistical Software, 27, 3, pp. 1–22. DOI: https://doi.org/10.18637/jss.v027.i03
Hyndman R.J., Koehler A.B. (2006) Another Look at Measures of Forecast Accuracy. International Journal of Forecasting, 22, 4, pp. 679–688. DOI: https://doi.org/10.1016/j.ijforecast.2006.03.001
Ivakhnenko A.G. (1968) The Group Method of Data Handling – a Rival of the Method of Stochastic Approximation. Soviet Automatic Control, 13, 3, pp. 43–55.
Ji S., Kim J., Im H. (2019) A Comparative Study of Bitcoin Price Prediction Using Deep Learning. Mathematics, 7, 10, p. 898. DOI: https://doi.org/10.3390/math7100898
John D., Binnewies S., Stantic B. (2024) Cryptocurrency Price Prediction Algorithms: A Survey and Future Directions [Report]. DOI: https://doi.org/10.20944/preprints202406.1864.v1
Khan F., Khan F., Shaikh P. (2023) Forecasting Returns Volatility of Cryptocurrency by Applying Various Deep Learning Algorithms. Future Business Journal, 9. DOI: https://doi.org/10.1186/s43093-023-00200-9
Kilimci H., Yildirim M., Kilimci Z. (2021) The Prediction of Short-Term Bitcoin Dollar Rate (BTC/USDT) using Deep and Hybrid Deep Learning Techniques. 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 633–637. DOI: https://doi.org/10.1109/ISMSIT52890.2021.9604741
Kilimci Z. (2020) Sentiment Analysis Based Direction Prediction in Bitcoin Using Deep Learning Algorithms and Word Embedding Models. International Journal of Intelligent Systems and Applications, 8, pp. 60–65. DOI: https://doi.org/10.18201/ijisae.2020261585
Kostin N., Belousov A. (2022) Implementation and Research of a Solution for Forecasting Crypto-currency Exchange Rate Dynamics Using Autoregressive Models. International Scientific Conference Proceedings “Advanced Information Technologies and Scientific Computing”, Publishing House of the Samara Scientific Center of the Russian Academy of Sciences, pp. 170–173 (In Russ.)
Mahfooz A., Phillips J. (2024) Conditional Forecasting of Bitcoin Prices Using Exogenous Variables. IEEE Access, 1, p. 1. DOI: https://doi.org/10.1109/ACCESS.2024.3381516
Marcellino M., Stock J.H., Watson M.W. (2006) A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series. Journal of Econometrics, 135(1–2), pp. 499–526. DOI: https://doi.org/10.1016/j.jeconom.2005.07.020
McNally S., Roche J., Caton S. (2018) Predicting the Price of Bitcoin Using Machine Learning. 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 339–343. DOI: https://doi.org/10.1109/PDP2018.2018.00060
Pabuçcu H., Ongan S., Ongan A. (2023) Forecasting the Movements of Bitcoin Prices: An Application of Machine Learning Algorithms [Report].
Pečiulis T., Ahmad N., Menegaki A.N., Bibi A. (2024) Forecasting of Cryptocurrencies: Mapping Trends, Influential Sources, and Research Themes. Journal of Forecasting, 43. DOI: https://doi.org/10.1002/for.3114
Radford A., Jozefowicz R., Sutskever I. (2017) Learning to Generate Reviews and Discovering Sentiment.
Radford A., Narasimhan K., Salimans T., Sutskever I. (2018) Improving Language Understanding by Generative Pre-Training.
Ranco G., Aleksovski D., Caldarelli G., Grčar M., Mozetic I. (2015) The Effects of Twitter Sentiment on Stock Price Returns. PloS one, 10. DOI: https://doi.org/10.1371/journal.pone.0138441
Seifaddini M., Habibdoust A. (2023) Improving Bitcoin Price Prediction Power by Time-Scale Decomposition and GMDH-Type Neural Network: A Comparison of Different Periods and Features. Journal of Mathematical Modeling (JMM), 11, 1.
Serafini G., Ping Y., Zhang Q., Brambilla M., Wang J., Hu Y., Li B. (2020) Sentiment-Driven Price Prediction of the Bitcoin based on Statistical and Deep Learning Approaches. International Joint Conference on Neural Networks (IJCNN), pp. 1–8. DOI: https://doi.org/10.1109/IJCNN48605.2020.9206704
Shamshad H., Ullah F., Ullah A., Kebande V.R., Ullah S., Al-Dhaqm A. (2023) Forecasting and Trading of the Stable Cryptocurrencies with Machine Learning and Deep Learning Algorithms for Market Conditions. IEEE Access 2023, 1, p. 99. DOI: https://doi.org/10.1109/ACCESS.2023.3327440
Shkanov B.A. (2024) Pricing Factors of Cryptocurrencies. Vestnik of Samara University. Economics and Management, 15, 1, pp. 225–237. (In Russ.) DOI: https://doi.org/10.18287/2542-0461-2024-15-3-225-237
Shkanov B.A. (2025) Comprehensive Approach to Portfolio Optimization Based on Modern Mathematical Methods. π-Economy,18 (2), pp. 179–201. (In Russ.)
Shkanov B., Alexandrov M. (2024) Social Influence, Market Manipulators, Hardware and Software As New Factors for Cryptocurrency Pricing: A Survey. Computación y Sistemas, 28 (3), pp. 1201–1207. DOI: https://doi.org/10.13053/cys-28-3-5196
Singh S., Bhat M. (2024) Transformer-Based Approach for Ethereum Price Prediction Using Cross-currency Correlation and Sentiment Analysis [Report].
Sridhar S., Sanagavarapu S. (2021) Multi-Head Self-Attention Transformer for Dogecoin Price Prediction. 14th International Conference on Human System Interaction (HSI) At: Gdańsk, Poland. DOI: https://doi.org/10.1109/HSI52170.2021.9538640
Tan X., Kashef R. (2019) Predicting the Closing Price of Cryptocurrencies: A Comparative Study. E-Learning and Information Systems. DATA '19: Proceedings of the Second International Conference on Data Science, pp. 1–5. DOI: https://doi.org/10.1145/3368691.3368728
Tandon S., Tripathi S., Saraswat P., Dabas C. (2019) Bitcoin Price Forecasting Using LSTM and 10-fold Cross Validation. International Conference on Signal Processing and Communication (ICSC). DOI: https://doi.org/10.1109/ICSC45622.2019.8938251
Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A., Kaiser L., Polosukhin I. (2017) Attention Is All You Need.
Wu C.H., Lu C.C., Ma Y.F., Lu R.S. (2018) A New Forecasting Framework for Bitcoin Price with LSTM. IEEE International Conference on Data Mining Workshops (ICDMW). DOI: https://doi.org/10.1109/ICDMW.2018.00032
