Fuzzy Clustering Algorithm for Trend Prediction of The Digital Currency Market
DOI:
https://doi.org/10.56294/sctconf20241094Keywords:
Fuzzy Clustering, Digital Currencies, Cryptocurrency, Fuzzy ModellingAbstract
Digital currencies, such as Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT), have been attracting the interest of investors and speculators. Over the last several years, the exponential growth in the value of digital currency has captured the interest of many individuals who see it as an attractive investment opportunity. After all, investors must deal with the expected volatility of Bitcoin prices as part of their investments. The future development of cryptocurrency can be challenging to forecast because of the extreme unpredictability and disorder of external events. In this research, fuzzy models for cryptocurrency price forecasting using a level set-based Fuzzy Clustering Based on Multi-Criteria Decision-Making (FC-MCDM). Compared to linguistic and functional fuzzy clustering, the construction and processing of fuzzy rules in a multi-criteria decision-making-based collection set differ. Based on level sets, the model produces the weighted average of the functions that active fuzzy rules provide as output. In the model's outputs, the activation levels of the fuzzy rules are represented directly by the output functions. Computational experiments are carried out to test the efficacy of the level-set approach for one-step-ahead prediction of cryptocurrency closing prices. Meanwhile, level set-based fuzzy clustering outperforms the other methods when the direction of price change evaluates performance
References
1. Bansal A, Singh A, Vats S, and Ahlawat K. Identifying Critical Transition in Bitcoin Market Using Topological Data Analysis and Clustering. In International Conference on Communication and Intelligent Systems, pp. 79-90. Singapore: Springer Nature Singapore.
2. Islam MS, Hossain E, Rahman A, Hossain MS, and Andersson K. A review on recent advancements in forex currency prediction. Algorithms, 13(8), pp. 186.
3. Hajek P, and Novotny J. Fuzzy rule-based prediction of gold prices using news affect. Expert Systems with Applications, 193, pp. 116487.
4. Cortez K, Rodríguez-García MDP, and Mongrut S. Exchange market liquidity prediction with the K-nearest neighbor approach: Crypto vs. fiat currencies. Mathematics, 9(1), pp. 56.
5. Hao PY, Kung CF, Chang CY, and Ou JB. Predicting stock price trends based on financial news articles and using a novel twin support vector machine with fuzzy hyperplane. Applied Soft Computing, 98, pp. 106806.
6. Šťastný T, Koudelka J, Bílková D, and Marek L. Clustering and Modelling of the Top 30 Cryptocurrency Prices Using Dynamic Time Warping and Machine Learning Methods. Mathematics, 10(19), pp. 3672.
7. Alonso-Monsalve S, Suárez-Cetrulo AL, Cervantes A, and Quintana D. Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Systems with Applications, 149, pp. 113250.
8. Singh J, Bowala S, Thavaneswaran A, Thulasiram R, and Mandal S. Data-Driven and Neuro-Volatility Fuzzy Forecasts for Cryptocurrencies. In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-8. IEEE.
9. Hernandez-Aguila A, García-Valdez M, Merelo-Guervós JJ, Castañón-Puga M, and López OC. Using Fuzzy inference systems for the creation of forex market predictive models. IEEE Access, 9, pp. 69391-69404.
10. Amiri A, Tavana M, and Arman H. An Integrated Fuzzy Analytic Network Process and Fuzzy Regression Method for Bitcoin Price Prediction. Internet of Things, 25, pp. 101027.
11. Cohen G. Algorithmic trading and financial forecasting using advanced artificial intelligence methodologies. Mathematics, 10(18), pp. 3302.
12. Chen W, Xu H, Jia L, and Gao Y. Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants. International Journal of Forecasting, 37(1), pp. 28-43.
13. Munusamy S, and Murugesan P. Modified dynamic fuzzy c-means clustering algorithm–Application in dynamic customer segmentation. Applied Intelligence, 50(6), pp. 1922-1942.
14. Awotunde JB, Ogundokun RO, Jimoh RG, Misra S, and Aro TO. Machine learning algorithm for cryptocurrencies price prediction. In Artificial intelligence for cyber security: methods, issues and possible horizons or opportunities, pp. 421-447. Cham: Springer International Publishing.
15. Liu J, Wei Y, and Xu H. Financial sequence prediction based on swarm intelligence algorithms of internet of things. Computational Economics, 59(4), pp. 1465-1480.
16. Shou MH, Wang ZX, Li DD, and Zhou YT. Forecasting the price trends of digital currency: a hybrid model integrating the stochastic index and grey Markov chain methods. Grey Systems: Theory and Application, 11(1), pp. 22-45.
17. Seo Y, and Hwang C. Predicting bitcoin market trend with deep learning models. Quantitative Bio-Science, 37(1), pp. 65-71.
18. Almeida J, Tata S, Moser A, and Smit V. Bitcoin prediction using ANN. Neural networks, 7, pp. 1-12.
19. Chowdhury R, Rahman MA, Rahman MS, and Mahdy MRC. Predicting and forecasting the price of constituents and index of cryptocurrency using machine learning. arXiv preprint arXiv:1905.08444, pp. 1-15.
20. Altan A, Karasu S, and Bekiros S. Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques. Chaos, Solitons & Fractals, 126, pp. 325-336.
21. https://www.kaggle.com/datasets/lasaljaywardena/global-cryptocurrency-price-database
Published
Issue
Section
License
Copyright (c) 2024 Suxia Sun, Yiyang Qin (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
