Bitcoin-Price-Prediction-Using-EMD-SVR

This project presents an ensemble method composed of Emprirical Mode Distribution (EMD) algorithm and Support Vector Regression (SVR) for Bitcoin price prediction by analyzing the time series data of Bitcoin process. Bitcoin price prediction is one of the most challenging tasks of time series forecasting due to the inherent non-linearity and nonstationary characteristics of the bitcoin market and financial time series. It has recently attracted considerable attention in the fields of economics, cryptography and computer science due to its inherent nature of combining encryption technology and monetary units. Here, we attempt to predict the Bitcoin price accurately taking into consideration various parameters that affect the Bitcoin value. Our data set consists of various blockchain parameters relating to the Bitcoin price and payment network over the course of five years recorded daily. Along with this, we also used the data set of 5 other cryptocurrencies to observe the relation of Bitcoin price to the price of these cryptocurrencies. The proposed EMD-SVR model is evaluated with recent one month price data set.