Prediction of Cryptocurrency prices using Transformers and Long Short term Neural Networks

Abstract

Cryptocurrencies have taken over the whole financial world with their high risk and high reward quality. Cryptocurrency markets are one of the most complex and volatile markets in the world. In this paper, we make an attempt to predict financial time series for popular cryptocurrencies like Ethereum, Binance coin and Bitcoin, using deep learning and multifractal detrended fluctuation analysis techniques. After testing for non-linearity in the financial time series of popular cryptocurrencies, it was revealed that cryptocurrency time series manifest latent relations, short term, and long term memory. The method uses Transformers and Long-short term Neural networks(LSTM) to forecast the prices of various cryptocurrencies. Although using LSTM along with transformers leads to longer computational times, but the predictive accuracy is better as compared to traditional regression neural networks and kNN forecasting models. Experimentation of the proposed model reveals that the deep learning model is profoundly efficient in predicting the intrinsic dynamics of cryptocurrency financial time series.