Authors - Jenat Arshad, Afruja Akter, Tanjina Akter, Kingkar Prosad Ghosh, Anupam Singha Abstract - Cryptocurrencies have emerged as a significant financial asset class, attracting global attention for their potential to disrupt traditional financial systems. Due to its extreme price volatility and ability to be traded without the assistance of a third party, cryptocurrencies have gained popularity among a wide range of individuals. This paper presents a comprehensive study of machine learning techniques, particularly deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Artificial Neural Network (ANN) in predicting cryptocurrency prices. Furthermore, this study addresses the security and privacy challenges inherent to blockchain technology, upon which cryptocurrencies operate. We predict the prices of popular cryptocurrencies like Bitcoin, and Ethereum, and lesser-known ones like Binancecoin, Litecoin, and Ripple through a hybrid deep learning model. This paper also compares cryptocurrency price prediction with machine learning models like GRU, ANN, and our proposed model Hybrid LSTM-GRU. The results demonstrate the efficacy of machine learning in price prediction, highlighting blockchain's potential to enhance security and privacy in financial transactions. Our model gives the value of MSE, RMSE, MAE and MAPE to determine the forecasting. We’ve also added the manual calculation for each metric and compared the actual price with the predicted price that our model gave.
Open Zoom
