Cryptocurrency Price Prediction using LSTM and Recurrent Neural Networks

Abstract

Cryptocurrency price prediction is most wanted by investors nowadays to get more money in cryptocurrency investment. All existing methods depicted in the survey for Cryptocurrencies price prediction are not suitable for real-time investment price prediction. To handle the above-mentioned issues, Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) is anticipated for Cryptocurrency price prediction. The proposed method depends on machine learning technique, mostly in monetary fields for forecasting stock prices. Min-Max Scaler is used for pre-processing, changing the numeric values to the common scale in the dataset. LSTM is an Artificial Recurrent Neural Network (RNN) model employed in the deep learning field, and here it is used for cryptocurrency price prediction. Recurrent Neural Network (RNN) using LS TM can be accomplished in the proposed model, which proceeds with a set of working out sequences by using an optimization procedure like gradient descent with back transmission through time to calculate the gradients required through the progression of optimization in order to change each weight of the LSTM network to perform error calculation at the output layer of LSTM with respect to the corresponding weight. The proposed strategy involves the result from the model, which is considered as the another contribution for a similar model.