Harnessing Machine Learning for Crypto-Currency Price Prediction: A Review - Ali, Zeravan Arif; Abdulazeez, Adnan M (International Journal of Research and Applied Technology (INJURATECH), 2024)
Despite their recent inception, cryptocurrencies have become globally recognized for their dispersal, diversity, and high market capitalization. This volatility developed into a challenge for investors looking to predict price movements. Thus, it has become an attractive investment opportunity. To increase prediction accuracy, researchers integrate machine learning algorithms with technical indicators. In this review, a systematic comparison has been employed to identify efficient algorithms, and researchers have employed statistical measures to make short- and long-term forecasts of decentralized money prices. Moreover, the paper highlights the results of researchers based on machine learning and deep learning methodologies on multiple types of cryptocurrencies like Bitcoin, Ethereum, Monero, etc. Lastly, the work emphasizes the limitations, gaps, and challenges facing researchers to take advantage of existing literature for future works.