Bitcoin Price Prediction Using Machine Learning and Deep Learning Algorithms

Abstract

A cryptocurrency or ‘coins' is a virtual asset which can be used as an alternative to physical currency via a computer network that is not reliant on the government or any bank, to uphold or maintain it. This has led to the creation of numerous brands of cryptocurrencies. It was only after the boom in their price in 2011 that they began to be regarded as an investment asset. Since these coins are highly volatile in their pricing, there is a need for a good prediction of their closing price on which investment decisions can be made. To address this requirement, this paper studies the relative performances of different machine learning algorithms for a well-known cryptocurrency - the ‘Bitcoin’. The performance measures of different machine learning models were undertaken to get the accuracy of the models for Bitcoin and results were obtained. The results show that the Auto Regressive Integrated Moving Average (ARIMA) is better than the other models and has the least mean absolute error. It is observed that the quality of training data and amount of the dataset used plays an important role for a successful prediction. When comparing the predicted value of Bitcoin through ARIMA with its actual value, the results obtained are found to be comparable for the entire four months of analysis.