Time series and graph analysis of Ethereum blockchain

• Main Author: Wang,Ye
• Other Authors: Anwitaman Datta
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/157055
• Institution: Nanyang Technological University

Cryptocurrencies have become increasingly popular with investors over the past few years as a means of transaction. It is primarily an investment asset that can benefit from and get significant returns. The risk of investing is not being able to make 100% accurate price predictions. Therefore, a high-accuracy price forecast is an artifact that every investor yearns for. However, the task is almost impossible to accomplish. Compared with fiat currencies and ordinary stock markets, cryptocurrencies are more volatile, more ups and downs, and more sensitive to the impact of economic factors. Their prices depend on interoperability with blockchain networks, market trends, social sentiment and even other cryptocurrencies. Therefore, traditional mathematical statistics cannot fully cover the complexities of cryptocurrency exchange rates. Researchers have had to turn to advanced machine learning techniques. Ethereum has become one of the most important cryptocurrencies in terms of transaction volume. Given its recent growth, the cryptocurrency community and researchers are interested in understanding the price of Ethereum. In this project, I propose a method for building machine learning models to predict Ethereum prices, achieving up to 96% short-term and long-term prediction accuracy. Using this method of forecasting and predicting, I propose a buying and selling strategy that helps investors decide whether to invest or sell, and when the time is right.