Prediction on Bitcoin Price Trends based on Machine Learning Algorithms

Abstract

With the rapid growth of technology, cryptocurrency like Bitcoin is attracting more and more attention. Its high volatility in prices creates many difficulties for predicting and there has been much work on this. This paper aims to provide a comparison of various machine learning models like linear regression, SVM, random forest, and neural networks for predicting the directions for Bitcoin close prices. The dataset used is from Jan 2012 to March 2021 and all four prices are used for predictions: Close, Open, High, and Low. Two different methods are used to fit the different types of machine learning algorithms: for regressors, close price predictions are first done and then construct in the predicted direction; for classifiers, direction predictions are done directly. Accuracy is used to do the comparison, which is the percentage of correct direction predictions made via the algorithm. It is shown that LSTM, a neural network algorithm generates the highest accuracy of about 58% and the random forest classifier has the lowest accuracy of about 55.47%.