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Predicting the Trends of Price for Ethereum Using Deep Learning Techniques

  • Deepak KumarEmail author
  • S. K. Rath
Conference paper
  • 11 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1056)

Abstract

This study intends to predict the trends of price for a cryptocurrency, i.e. Ethereum based on deep learning techniques considering its trends on time series particularly. This study analyses how deep learning techniques such as multi-layer perceptron (MLP) and long short-term memory (LSTM) help in predicting the price trends of Ethereum. These techniques have been applied based on historical data that were computed per day, hour and minute wise. The dataset is sourced from the CoinDesk repository. The performance of the obtained models is critically assessed using statistical indicators like mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE).

Keywords

Deep learning Ethereum MLP LSTM Cryptocurrency 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyRourkelaIndia

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