Prediction of Bitcoin Price Based on LSTM

Abstract

In response to the rise of the bitcoin market, the nonlinear variation of bitcoin price has always been the center of research in the community. Using bitcoin transaction data from 2014 to 2017, this study removes the uncontrollability and unpredictability of external factors and discusses the relationship between the predict and actual price of a single-feature LSTM model and a multi-feature LSTM model that incorporates thermodynamic chart to point out potentially highly correlated variables for the bitcoin price itself only, sets up a one-day prior algorithm, uses Python 3.7, Keras and LSTM tools to plot line plots of predicted and true prices and compare the accuracy of both. We conclude that the LSTM prediction is better with multiple features, which can greatly reduce the error and hedge the risk. Even in the chance case of more drastic fluctuations, the prediction is still better, which improves the utility and applicability of the model.