Prediction of Cryptocurrency using LSTM and XGBoost

Abstract

Digital currency arose because of progress in financial technology and created opportunities for profitable cryptocurrency investment. The high instability of Bitcoin made cryptocurrency trading so worthwhile in the last few years. Investors are looking for a secure mechanism to forecast the cryptocurrency price fluctuations in the market that will fuel their investment strategies. The algorithms such as random forest, Bayesian neural network, or long-short-term-memory (LSTM) neural network analyze the price fluctuations of the cryptocurrencies through the historical data and attain high precision. This paper analyzed a few shortcomings of the LSTM network and explored the vital parameters to overcome them. This paper employed the XGBoost algorithm to predict cryptocurrency prices better and found a better mean value deviation error than LSTM.