An Integrated Framework for Cryptocurrency Price Forecasting and Anomaly Detection Using Machine Learning

Summary

The correct prediction of cryptocurrency costs is essential as a result of volatility and complexity of digital asset markets, which pose important challenges to merchants, buyers, and researchers. This analysis addresses these challenges by leveraging machine studying and deep studying strategies to forecast closing costs for cryptocurrencies, specializing in Bitcoin, Ethereum, Binance Coin, and Litecoin cryptocurrency datasets. A Random Forest ensemble studying algorithm, a Gradient Boosting mannequin, and a feedforward neural community have been carried out to deal with the complexities in cryptocurrency information. A Z-Rating-based anomaly detection framework was built-in to categorise closing costs as regular or irregular, aiding in figuring out important market occasions. Analysis metrics, such because the Imply Squared Error (MSE), Root Imply Squared Error (RMSE), Imply Absolute Error (MAE), and R-squared (R²), reveal the superior precision and reliability of the Random Forest and Gradient Boosting fashions. The deep studying mannequin signifies sturdy generalization capabilities, suggesting potential benefits on extra advanced datasets. These findings spotlight the significance of mixing superior machine studying strategies and cryptocurrencies to develop a strong framework for cryptocurrency forecasting and anomaly detection.