Temporal Data Integration and Forecasting Prices for Bitcoin and Ethereum using Machine Learning and Deep Learning Techniques

Abstract

Bitcoin (BTC) and Ethereum (ETH) price and trends prediction is performed by long short-term memory (LSTM) networks, gated recurrent unit (GRU) and Random Forest machine learning algorithm, the authors explain. Feature selection techniques were effectively and widely adopted to preprocess and feed real cryptocurrency market data as input data. LSTM performs have an accuracy of 96%, GRU performs have accuracy of 97%, and Random forest 98%, meaning they are satisfactory in predicting cryptocurrency trends theme. These models were used to construct two real worlds advert based knowledge driven investment strategies which were simulated through the period under study and show the potential of this class of models. Results of which showed across different times period cases how well your prediction works [7], and all pointed out on the huge probably availability of the presence of profit making opportunity and hence the way in which your predictive way of prediction the unpredictable market crypto currency.