Deep Learning Ethereum Token Price Prediction on Dynamic Network and Time Series Analysis

Abstract

Ethereum has recently surged in popularity, as it can hold various digital tokens and decentralized applications.This paper aims to predict UNI's price in USD through dynamic network analysis and time-series analysis.Previous research in this field rarely considers comprehensive network analysis while predicting token price.This paper puts forward a strengthened Bidirectional LSTM model that includes token economical features and network features.We use Root Mean Squared Error (RMSE) to verify the validity and compare it with other LSTM and GRU models on performance.Lastly, a logarithm difference method for data preprocessing was introduced to resolve the lag problems.