Fuzzy Clustering Algorithm for Trend Prediction of The Digital Currency Market

Abstract

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