Time Series Analysis and prediction of bitcoin using Long Short Term Memory Neural Network

Abstract

Bitcoin is the first digital currency that uses decentralization to solve the issue of trust in performing the functions of a digital currency successfully. This digital currency has shown extraordinary growth and intermittent plunge in value and market capitalization over time. This makes it important to understand what determines the volatility of bitcoin and to what extent they are predictable. Long Short Term Memory Neural Networks (LSTM-NN) have recently grown popular for time series prediction systems but there has been no consensus on methods to model time series inputs for LSTMs, this paper proposes the need for this problem to be solved by conducting an experimental research on the efficacy of an LSTM-NN given the form of its time-series input features.