[1] Matviychuk A. Fuzzy logic approach to identification and forecasting of financial time series using Elliott wave theory // Fuzzy Economic Review. — 2006. — Vol. 11, no. 02. — P. 45—54. — URL:https://doi.org/10.25102/fer.2006.02.04](https://doi.org/10.25102/fer.2006.02.04).
[2] Bielinskyi A., Ivanov B., Petrenko C. Fuzzy time series forecasting using semantic artificial intelligence tools // Neuro-Fuzzy Modeling Techniques in Economics. — 2022. — No. 11. — P. 157—198. — URL:[https://doi.org/10.33111/nfmte.2022.157](https://doi.org/10.33111/nfmte.2022.157).
[3] Rhif M., Hleli H., Smara Y. Wavelet transform application for non-stationary time-series analysis: A Review // Applied Sciences. — 2019. — Vol. 9, no. 7. — P. 1345. — URL: [https://doi.org/10.3390/app9071345](https://doi.org/10.3390/app9071345).
[4] Surmann H., Selenschtschikow A. Automatic generation of fuzzy logic rule bases: Examples I // Proceedings of the First International ICSC Conference on Neuro-Fuzzy Technologies (16-19 January 2002). — Vienna, 2002. — P. 75-81.
[5] Glybovets M., Gulaeva N. Evolutionary algorithms. — Kyiv: NaUKMA, 2013. — 828 p.
[6] Matviychuk A. V. Artificial Intelligence in Economics: Neural Networks, Fuzzy Logic: Monograph. — Kyiv: KNEU, 2011. — 439 p.
[7] Matviychuk A. V. Research on the dependence of the quality of forecasting security prices by neural networks on the form of input data presentation // Collection of scientific papers of Cherkasy State Technological University. Series: Economic Sciences. — 2003. — Issue 8. — P. 147—156.
[8] Tayib H., Abdulazeez M. A. A Review of Bitcoin Price Prediction Based on Deep Learning Algorithms // The Indonesian Journal of Computer Science. — 2024. — Vol. 13, no. 2. — P. 45-58. — URL: [https://doi.org/10.33022/ijcs.v13i2.3858](https://doi.org/10.33022/ijcs.v13i2.3858).
[9] Top 10 Cryptocurrencies Historical Dataset [Електронний ресурс] // Kaggle. — URL: [https://www.kaggle.com/datasets/kaushiksuresh147/top-10-cryptocurrencies-historicaldataset](https://www.kaggle.com/datasets/kaushiksuresh147/top-10-cryptocurrencies-historicaldataset).
[10] Zhang X., Huang Z., Wu Y., Lu X., Qi E., Chen Y., Xue Z., Wang Q., Wang P., Wang W. Multi-period Learning for Financial Time Series Forecasting // Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD —25). — New York; ACM, 2025. — P. 2848—2859. —
URL: [https://doi.org/10.1145/3690624.3709422](https://doi.org/10.1145/3690624.3709422).
[11] Badar W., Ramzan S., Raza A., Fitriyani N. L., Syafrudin M., Lee S. W. Enhanced Interpretable Forecasting of Cryptocurrency Prices Using Autoencoder Features and a Hybrid CNN-LSTM Model // Mathematics. — 2025. — Vol. 13, no. 12. — Art. 1908. — URL:
[https://doi.org/10.3390/math13121908](https://doi.org/10.3390/math13121908).
[12] Zeng Z., Kaur R., Siddagangappa S., Rahimi S., Balch T., Veloso M. Financial Time Series Forecasting using CNN and Transformer // arXiv preprint arXiv:2304.04912. — 2023. — URL:
[https://arxiv.org/abs/2304.04912](https://arxiv.org/abs/2304.04912)).
[13] Kong X., Chen Z., Liu W. et al. Deep learning for time series forecasting: a survey // Int. J. Mach. Learn. & Cybern. — 2025. — URL: [https://doi.org/10.1007/s13042-025-02560-w](https://doi.org/10.1007/s13042-025-02560-w).

[14] Kilic D. K., Ugur O. Hybrid wavelet-neural network models for time series
// Applied Soft Computing. — 2023. — Vol. 144. — Art. 110469. — URL:
[https://doi.org/10.1016/j.asoc.2023.110469](https://doi.org/10.1016/j.asoc.2023.110469) .
[15] Li Y., Dai W. Bitcoin price forecasting method based on CNN-LSTM hybrid neural network model // The Journal of Engineering. — 2020. — P. 344—347. — URL: [https://doi.org/10.1049/joe.2019.1203](https://doi.org/10.1049/joe.2019.1203).
[16] Wu J., Zhang X., Huang F., Zhou H., Chandra R. Review of deep learning models for crypto price prediction: Implementation and evaluation // arXiv preprint arXiv:2405.11431. — 2024. — URL:
[https://arxiv.org/abs/2405.11431](https://arxiv.org/abs/2405.11431).
[17] Ahmed B., Abedin M. Z., Hajek P., Yuan K. Cryptocurrency price forecasting — A comparative analysis of ensemble learning and deep learning methods // International Review of Financial Analysis. — 2024. — Vol. 92. — Art. 103055. — URL: [https://doi.org/10.1016/j.irfa.2023.103055](https://doi.org/10.1016/j.irfa.2023.103055).