Deep Learning Ethereum Token Price Prediction with Network Motif Analysis

Abstract

In this paper, we apply Long Short-Term Memory (LSTM) neural networks to model the token price time series data which incorporate the local topological measures of investor transaction network and market summaries. In addition, we propose a novel LSTM-based model using the leave-one-out cross-validation technique and utilizing the network motif analysis. The numerical results show that the proposed LSTM-based model could significantly improve the performance of the prediction of token price compared to the benchmark LSTM-based models and deep portfolios regardless of the training and testing data split ratio. Some concluding remarks and future research directions are provided.