Bitcoin Vision: Using Machine Learning and Data Mining to Predict the Short-Term and Long-Term Price of Bitcoin

• Published in: Webology Vol. 18; no. SI05; pp. 751 - 760
• Main Authors: Millikan, Evan, Subramanian, Preethi, Joseph, Minnu Helen
• Format: Journal Article
• Language: English
• Published: Tehran Dr. Alireza Noruzi, University of Tehran, Department of Library and Information Science 30.10.2021
• Subjects: Blockchain; Deep learning; Digital currencies; Financial institutions; Investments; Literature reviews; Machine learning; Securities markets; Sentiment analysis; Third party; Trends
• ISSN: 1735-188X; 1735-188X
• DOI: 10.14704/WEB/V18SI05/WEB18259

Cryptocurrencies are non-physical currency that solely exist as represented by 0s and 1s within the world of computers. One of the most popular cryptocurrencies in the market right now being Bitcoin, was first invented to solve the inherent problem with using traditional currency when purchasing online. However, unexpectedly Bitcoin soon found itself to be one of the most profitable investment opportunities to be hedged on with its yearly growth unrivaled by any traditional investment product such as stocks, bonds, or real-estate. However, unlike the stock market which has been the subject of multitude of research papers, the cryptocurrency market has not been treated the same way and as such there is still a huge opportunity open in this industry. Thus, Bitcoin Vision wants to utilize this opportunity and propose the use of machine learning and deep learning architecture to predict the price movement trend of bitcoin (up or down) for the short-term prediction and predict the price of bitcoin for the long-term prediction. Being able to predict the future of the market prove to be useful in the stock market and as such this paper decide to replicate that opportunity to be presented in the cryptocurrency market as well. In this literature review paper, we have proposed the comparison of state-of-the-art deep learning model such as Long Short-Term Memory (LSTM) with traditionally successful machine learning model such as Random Forest and ARIMA to find out which model provide the best result.