A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices

Abstract

Bitcoin as an asset class has received phenomenal investor attention and is considered to have similar characteristics like gold. This study aims to analyze the price behavior of bitcoin and apply machine learning algorithm for its prediction. Understanding the nature of Bitcoin price series is a multi-scale problem, and it can be best examined by analyzing its compositional characteristics. This study uses complete empirical ensemble mode decomposition (CEEMD) to analyze the nature of Bitcoin price series. Daily Bitcoin prices from 2012 to 2018 are used to perform CEEMD to identify the short term, medium term, and long-term trend in the Bitcoin price series. The study uses support vector machine (SVM) learning algorithm to find whether it can predict Bitcoin prices and finds that SVM predicts five steps ahead Bitcoin prices for the short term, medium term, long term, and overall Bitcoin price level.