Prediction of Bitcoin prices' trends with ensemble learning models

Abstract

People used to invest in the stock and fund markets in the past and now have paid more attention to cryptocurrencies’ market, in which Bitcoin is the most famous and classic underlying asset. In this research, in order to improve the effects of stock prediction, difference of close price and moving average lines will be used as the labels of ensemble learning models. With the predicting results of each label, formulas are used to derive the close price in the next day. The last results show that models have less errors as the moving average prices are used as labels. Based on the models built in this research, people manage to predict the prices’ trends of Bitcoin more accurately and make investment decisions that will yield additional returns.