Short-term prediction for Ethereum with Deep Neural Networks

  • Eduardo José Costa Lopes Centro Universitário FEI
  • Reinaldo Augusto da Costa Bianchi Centro Universitário FEI


The main contribution of this research is to investigate whether an Artificial Neural Network is an option to predict Ethereum cryptocurrency close price on a time constrained scenario. The ANN training time and time lagged data availability are considered as constraints on finding the fastest and the most accurate regression model using ARIMA results as a baseline. As part of the study, hourly aggregated data is processed to generate a step-ahead forecast and then processing time is compared for each architecture. Previous work related to cryptocurrency forecasting usually focus the analysis only on accuracy, and use coarser data granularity. Results have shown that convolutional neural networks over performed other architectures for accuracy and time objectives.

Palavras-chave: Cryptocurrency, Ethereum, Neural networks, Deep Learning, Forecasting


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LOPES, Eduardo José Costa; BIANCHI, Reinaldo Augusto da Costa. Short-term prediction for Ethereum with Deep Neural Networks. In: BRAZILIAN WORKSHOP ON ARTIFICIAL INTELLIGENCE IN FINANCE (BWAIF), 1. , 2022, Niterói. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 1-12. DOI: