Hyper Parameter Classification on Deep Learning Model for Cryptocurrency Price Prediction

Abstract

Hyperparameter configurations highly affected the accuracy of the deep learning model. This study focuses on finding an appropriate parameter set that can apply to the Long Short-Term Memory (LSTM) and GRU (Gated Recurrent Unit) for cryptocurrency price prediction. The 80% portion of the data set is composed of an Open-high-low-close (OHLC), movement in the price over time, considered a training data set to predict the remaining 20% of OHLC. Our method classified several appropriate hyperparameter sets, leading to a high accuracy in terms of root mean square error (RMSE) on varying conditions, including number of layers, epochs, and batch size.