Machine Learning-Based Cryptocurrency Prediction: Enhancing Market Forecasting with Advanced Predictive Models - Islam, Md Shahidul; Bashir, Monjira; Rahman, Siddikur; Al Montaser, Md Abdullah; Bortty, Joy Chakra; Nishan, Araf; Haque, Muhammad Rafiuddin (Journal of Ecohumanism, 2025)
The cryptocurrency market, with its record volatility and breakneck speed, is a revolutionary phenomenon that is reshaping the entire world’s landscape. Unlike regular markets, cryptocurrencies undergo unprecedented volatility caused by a complex interaction of factors ranging from speculative trading to updates in regulations, technological innovations, and macroeconomic trends. The central objective of this research was to develop and evaluate machine learning-driven models of cryptocurrency price trend forecasting. The focus of this research project revolved around prominent cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), and other prominent altcoins, within the United States. The dataset employed in this analysis comprises vast historical price data, trading volumes, and key market indicators of major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), and other major altcoins. Historical price data is presented in terms of daily, hourly, and minute-level opening, closing, high, and low prices, providing detailed insights into temporal price behavior. Trading volumes, which reflect the intensity of trading action, are also provided to represent liquidity and investor participation behavior. The dataset also includes various market indicators, i.e., moving averages, relative strength index (RSI), Bollinger Bands, and other technical indicators, which play a pivotal role in establishing market patterns and momentum. Three models are chosen in this study: Logistic Regression, Random Forest Classifier, and XG Boost Classifier. For classification models, accuracy, precision, recall, and F1-score metrics are employed to evaluate the performance of the models in terms of predicting the directions of the markets (e.g., upward or downward directions). With the highest accuracy, Logistic Regression was the best-performing of the models tested, showing its relative superiority. The integration of AI forecasts into cryptocurrency trading has the potential to revolutionize the United States financial markets by providing traders and institutional investors with advanced tools to make decisions. The use of AI tools in cryptocurrency trading also has significant implications for United States regulation compliance. The integration of machine learning tools within cryptocurrency trading platforms is a significant step towards unleashing the true potential of AI in the financial markets. The field of AI-based cryptocurrency forecasting offers numerous areas of future research with the potential to break through present limitations and unlock new paths of market analysis. One of those areas is the use of deep learning models, i.e., Long Short-Term Memory (LSTM) networks, for time-series cryptocurrency forecasting.