Bitcoin price forecasting based on historical data

Abstract

Over the past few years, Bitcoin has been a topic of interest of many, from academic researchers to trade investors. Bitcoin is the first as well as the most popular cryptocurrency till date. Since its launch in 2009, it has become widely popular amongst various kinds of people for its trading system without the need of a third party and also due to high volatility of Bitcoin price. In this thesis, our aim is to be able to propose a suitable model that can predict the market price of Bitcoin best by applying a few statistical analysis. We have used Time series method specially Autoregressive Integrated Moving Average (ARIMA) model which can absolutely be called "learning algorithms" and be considered as a part of machine learning (ML) similarly with respect to regression. The work, at last could acquire the accuracy for deciding volatility in weighted costs, with an exactness of 91%.